{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:58:06.468311",
     "start_time": "2016-08-17T07:58:04.883583"
    },
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pprint\n",
    "from cohorts.functions import *\n",
    "import lifelines as ll\n",
    "import patsy\n",
    "import functools\n",
    "import survivalstan\n",
    "from cohorts.utils import strip_column_names\n",
    "import scipy\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## set seeds for stan & rngs, to aid in reproducibility\n",
    "## (note: only reproducible within a machine, not across machines)\n",
    "\n",
    "seed = 91038753\n",
    "import random\n",
    "random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:58:06.519332",
     "start_time": "2016-08-17T07:58:06.499244"
    },
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from utils import data\n",
    "import scipy\n",
    "from utils import paper\n",
    "from utils.extra_functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# prep data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:58:11.840853",
     "start_time": "2016-08-17T07:58:06.520959"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "{'dataframe_hash': 7698303973572390439,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort(join_with=[\"ensembl_coverage\",\"tcr_peripheral_a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:21.487992",
     "start_time": "2016-08-17T07:58:11.931783"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    }
   ],
   "source": [
    "def tcell_fraction(row):\n",
    "    return row[\"T-cell fraction\"]\n",
    "\n",
    "def peripheral_clonality_a(row):\n",
    "    return row['Clonality']\n",
    "\n",
    "cols, d = cohort.as_dataframe([snv_count,\n",
    "                               missense_snv_count,\n",
    "                               neoantigen_count,\n",
    "                               expressed_exonic_snv_count,\n",
    "                               expressed_missense_snv_count,\n",
    "                               expressed_neoantigen_count,\n",
    "                               exonic_snv_count,\n",
    "                               peripheral_clonality_a,\n",
    "                               tcell_fraction,\n",
    "                               ],\n",
    "                              rename_cols=True,\n",
    "                              return_cols=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:21.537104",
     "start_time": "2016-08-17T07:59:21.513702"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['snv_count',\n",
       " 'missense_snv_count',\n",
       " 'neoantigen_count',\n",
       " 'expressed_exonic_snv_count',\n",
       " 'expressed_missense_snv_count',\n",
       " 'expressed_neoantigen_count',\n",
       " 'exonic_snv_count',\n",
       " 'peripheral_clonality_a',\n",
       " 'tcell_fraction']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## construct/rescale variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:21.559557",
     "start_time": "2016-08-17T07:59:21.539192"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## add/modify count variables\n",
    "d['nonexonic_snv_count'] = d.snv_count - d.exonic_snv_count\n",
    "cols.append('nonexonic_snv_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.132709",
     "start_time": "2016-08-17T07:59:22.081884"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## create 'observed', log-transformed & centered versions of variables (not normalized by MB)\n",
    "for col in cols:\n",
    "    observed_col = 'observed_{}'.format(col)\n",
    "    log_col = 'log_{}'.format(col)\n",
    "    log_col_centered = 'log_{}_centered'.format(col)\n",
    "    log_col_rescaled = 'log_{}_rescaled'.format(col)\n",
    "    d[observed_col] = d[col]*d['mb']\n",
    "    d[log_col] = np.log1p(d[observed_col])\n",
    "    d[log_col_centered] = d[log_col] - np.mean(d[log_col])\n",
    "    d[log_col_rescaled] = d[log_col_centered]/np.std(d[log_col_centered])\n",
    "\n",
    "## save key vars in list for future use\n",
    "vars_centered = ['log_{}_centered'.format(col) for col in cols]\n",
    "vars_rescaled = ['log_{}_rescaled'.format(col) for col in cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.174374",
     "start_time": "2016-08-17T07:59:22.134744"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## construct new variables for key ratios / comparisons\n",
    "\n",
    "# what proportion of X are expressed?\n",
    "d['exonic_expression_ratio'] = d.expressed_exonic_snv_count / d.exonic_snv_count\n",
    "d['missense_expression_ratio'] = d.expressed_missense_snv_count / d.missense_snv_count\n",
    "d['neoantigen_expression_ratio'] = d.expressed_neoantigen_count / d.neoantigen_count\n",
    "\n",
    "# d['expressed_missense2snv_ratio'] = d.expressed_missense_snv_count / d.expressed_snv_count\n",
    "d['missense2exonic_snv_ratio'] = d.missense_snv_count / d.exonic_snv_count\n",
    "d['expressed_neoantigen2missense_ratio'] = d.expressed_neoantigen_count / d.expressed_missense_snv_count\n",
    "\n",
    "extra_cols = ['missense_expression_ratio','neoantigen_expression_ratio', 'exonic_expression_ratio', 'missense2exonic_snv_ratio','expressed_neoantigen2missense_ratio']\n",
    "## create recentered versions of ratios\n",
    "for col in extra_cols:\n",
    "    col_centered = '{}_centered'.format(col)\n",
    "    col_rescaled = '{}_rescaled'.format(col)\n",
    "    d[col_centered] = d[col] - np.mean(d[col])\n",
    "    d[col_rescaled] = d[col_centered]/np.std(d[col_centered])\n",
    "\n",
    "## append extra-cols to key var lists\n",
    "vars_centered.extend(['{}_centered'.format(col) for col in extra_cols])\n",
    "vars_rescaled.extend(['{}_rescaled'.format(col) for col in extra_cols])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## center variables by mean within PD-L1 group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.220909",
     "start_time": "2016-08-17T07:59:22.199735"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## center variables by group\n",
    "metrics = list(cols)\n",
    "metrics.extend(extra_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.242582",
     "start_time": "2016-08-17T07:59:22.222780"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "metrics2 = list(metrics)\n",
    "metrics2.extend(['pd_l1'])\n",
    "assert(not 'pd_l1' in metrics)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.275217",
     "start_time": "2016-08-17T07:59:22.244289"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bygrp = d.loc[:, metrics2]\n",
    "bygrp = bygrp.groupby('pd_l1').transform(lambda x: x - x.mean())\n",
    "bygrp['patient_id'] = d.patient_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.301832",
     "start_time": "2016-08-17T07:59:22.276693"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(d, bygrp, on = 'patient_id', suffixes = ['', '_centered_by_pd_l1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.354589",
     "start_time": "2016-08-17T07:59:22.303446"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# univariate survival analysis of revised metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.420738",
     "start_time": "2016-08-17T07:59:22.356618"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## prep dflong_pfs which will be used for survival analysis using stan\n",
    "df_long_pfs = survivalstan.prep_data_long_surv(df = df, event_col = 'is_progressed_or_deceased', time_col = 'pfs')\n",
    "df_long_os = survivalstan.prep_data_long_surv(df = df, event_col = 'is_deceased', time_col = 'os')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.448676",
     "start_time": "2016-08-17T07:59:22.422528"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_univariate_coefdata(model_list, metrics=None):\n",
    "    coefdata = list()\n",
    "    [coefdata.append(model['coefs']) for model in model_list]\n",
    "    coefdata = pd.concat(coefdata)\n",
    "    coefdata['expressed'] = coefdata.variable.str.contains('expressed')\n",
    "    coefdata['metric'] = coefdata.variable.str.replace('expressed_','').str.replace('log_','').str.replace('_centered','')\n",
    "    coefdata['ratio'] = coefdata.variable.str.contains('ratio')\n",
    "    coefdata['Odds Ratio'] = np.exp(coefdata.value)\n",
    "    coefdata['is_expressed'] = coefdata.expressed.apply(lambda x: 'expressed variants' if x==True else 'all variants')\n",
    "    if metrics:\n",
    "        coefdata = coefdata.loc[coefdata.metric.isin(metrics),:]\n",
    "    else:\n",
    "        coefdata = coefdata.loc[coefdata.ratio == False,:]\n",
    "    return(coefdata)\n",
    "\n",
    "def univariate_coefplot(model_list, metrics=None, hue='is_expressed'):\n",
    "    coefdata = extract_univariate_coefdata(model_list=model_list, metrics=metrics)\n",
    "    sb.boxplot(data=coefdata, x='Odds Ratio', y='metric', hue=hue, fliersize=0, whis=[2.5, 97.5],)\n",
    "    plt.xlim([0, 4])\n",
    "    plt.vlines(x=1, ymin=-10, ymax=10, linestyles='--')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    return(coefdata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using unstructured hazard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.469757",
     "start_time": "2016-08-17T07:59:22.450492"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survstan_pfs = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df_long_pfs,\n",
    "    model_code = survivalstan.models.pem_survival_model_unstructured,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    sample_col = 'patient_id',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    seed = seed,\n",
    "    )\n",
    "\n",
    "survstan_os = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df_long_os,\n",
    "    model_code = survivalstan.models.pem_survival_model_unstructured,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    sample_col = 'patient_id',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    seed = seed,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analysis of PFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:24:50.374509",
     "start_time": "2016-08-17T07:59:22.471397"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 60.963 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tavi/miniconda2/lib/python2.7/site-packages/stanity/psis.py:228: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  elif sort == 'in-place':\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/stanity/psis.py:246: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  bs /= 3 * x[sort[np.floor(n/4 + 0.5) - 1]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 19.609 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 12.977 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 13.439 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "Ran in 14.603 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 15.573 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 19.176 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "Ran in 13.285 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 15.681 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 16.408 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 16.875 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 16.543 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 15.677 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 15.983 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 11.649 sec.\n"
     ]
    }
   ],
   "source": [
    "univariate_models = list()\n",
    "for var in vars_centered:\n",
    "    univariate_models.append(survstan_pfs(formula = var, model_cohort = 'univariate survival analysis for PFS, unstructured hazard'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:24:57.110050",
     "start_time": "2016-08-17T08:24:50.376499"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-metrics-pfs-all}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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WwNvbG3Z2dvDw8EBQUBBSU1OFMhUVFdi2bRv8/f1hZ2eHnj17Yu3atUp3Z9QpKCgAAJib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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9b1a69b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{univariate-metrics-pfs-all}}}')\n",
    "coefdata = univariate_coefplot(univariate_models,\n",
    "                              metrics=['missense_snv_count',\n",
    "                                       'exonic_snv_count',\n",
    "                                       'neoantigen_count',\n",
    "                                       'peripheral_clonality_a',\n",
    "                                       'tcell_fraction',\n",
    "                                      ])\n",
    "_ = plt.xlim([0, 2.5])\n",
    "_ = plt.yticks([0, 1, 2, 3, 4],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "                'log(Baseline Peripheral TCR Clonality)',\n",
    "                'log(TIL Fraction)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-metrics-pfs}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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3jceysrIS5r2Eh4ejYcOGSEtLw4MHD+Dp6YkTJ07A1dUVTZo0gampKfT09CCRSPDvf/8b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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9995b8c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{univariate-metrics-pfs}}}')\n",
    "coefdata = univariate_coefplot(univariate_models,\n",
    "                               metrics=['missense_snv_count','exonic_snv_count','neoantigen_count'])\n",
    "_ = plt.xlim([0, 2.5])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare distribution of posterior draws for expressed vs total variant counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-ratios-pfs}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tavi/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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r9f0Znjx5ArVajQsXLmgtCw0NxfDhww1QFRER/VXoNTVCoVAgISEBdnZ2knZj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\naSQmwkRERESkkf4f6LwIh9tT5dEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9ac2e1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look at expression ratios, separately\n",
    "print('{{{univariate-ratios-pfs}}}')\n",
    "coefdata = univariate_coefplot(univariate_models,\n",
    "                               metrics=[x for x in extra_cols if 'expression_ratio' in x], hue=None)\n",
    "_ = plt.xlim([0, 2.5])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['Expression Rate of Missense SNVs',\n",
    "                'Expression Rate of Predicted Neoantigens',\n",
    "                'Expression Rate of Exonic SNVs',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-ratios-pfs-original}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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KytI2t7BmzZq4ffu2aJmlpaXGpdu8vDyoVCqtl15lMhnu3bsn1FFcXIxt27Zh8+bNMDQ0\nhK+vLyIiImBnZ4e6deti3bp1WLNmDaZMmYInT57A2dkZkyZNEt0UVdb9+/dRvXp1jZFqKysrqFQq\nYd1q1atXF/1dfXPdkydPtPZfVsOGDTFv3jyUlJTg4sWLiI6Oxvjx47F7927UqFEDwPMR91GjRiE3\nNxehoaFwcHBA5cqV8csvv2DVqlWvXY86DH/11Vf497//LXpNJpOhdu3aL32velu1PVnB2tr6tfNM\nddGjRw+MHDkS69evR82aNdGiRQud3qfLsU1MTIRSqcTixYtx9+5d2NvbY9iwYejfvz+A51cfrl69\nisaNG2v0X/Y8Uys7og88P9Zl5+yOHj0alStXRmpqKlavXg1TU1N8+umnmDJlCiwsLHQ+r1+latWq\n6Ny5s/Bzt3//fowdOxZxcXFYunSp1veEhYWhT58+SE5Oxscff6wxHxp4PpVh8+bN+Omnn/DZZ58h\nJSUF1tbWaNmypdDG3NwcGzZsQHx8PGbPno2CggI0aNAAoaGhaN++/Wtrp3+OgoICFBUVYejQoXqt\nIzc3F+/L84SKARTm5upln+Tm5qJy5crvfL307jHsVpCioiJMnToVjo6OuHLlCqKjo7VeBtc24piX\nl6cxP1TbHEULCwvIZDIkJCRonZdYNnT06dMHffr0wcOHD5Geno4FCxYgLCwMW7duBQB4eHjAw8MD\nT58+xYkTJ7B06VKMHDkS+/fv1wgvwPPwev/+fZSUlIgCb25ursa631SVKlWEO+CdnZ1hZ2eHQYMG\nQalUYvr06QCAq1ev4uzZs4iOjhaNsP766686rUNdb1hYmCi4qBkbG7/2veptLysnJwdNmjTRqYZX\nadmyJSwtLYUpIi+bs6rN646tvb09FixYAAD4z3/+g40bN+Lrr7+Gvb09fHx8YGFhgZo1a2LatGla\nA6CDg0O5tsXQ0BDDhw/H8OHDkZeXh99++w3z58/HkydPsHjx4nKd17ry9/eHQqF46cgu8PzcUk8h\nmjVr1kvbODg4ICUlBf7+/jh06BAGDx6scTwUCgXi4uLw7NkznDlzBqtWrcLEiRORkpKC+vXrl7t+\nInp/qe+v4VMZPhwMuxVk7ty5yMnJwa5du/Dbb78hMjISrVu3RqtWrUTtMjMzcfv2bSHcFhQU4MCB\nA6L5ky/TqlUrGBgYIDs7W+eRvqpVq6Jjx444deqUEHTLMjY2hqenJ4YPH46QkBBcv35da7jw8PDA\nunXr8NNPP4nCZWpqKkxMTODq6iosK08w04WnpyfatWuH5ORkBAcHw9bWFoWFhQDEzzt++vQpdu/e\nrfF+Y2Njjakc//rXv2BnZ4eLFy9ixIgR5arHwcEBVlZW+PHHH9G7d29h+YkTJ5CdnY1hw4aVqz9t\nZDIZQkJCcPDgQfTq1etv9aHLsVUoFJgyZQqSk5Nx4cIF+Pj4wMfHB9999x0++ugj4Rm1FaVmzZrC\ntAP1Uy/+znmtdu/ePZibm2tccXj8+DFu3ryp8USRF02YMAE9evTAxo0bX3re9uzZE0uWLMHq1atR\nWlr6yiklBgYGcHZ2xrhx4/Drr7/ir7/+Ytglgbm5OczNzZGQkKDXOoYOHYpHd+7otQY1EwBmVlZ6\n2Sf6HmGnd4dh9zVUKhXOnTundV6rk5MTDAwM8PPPP2Pbtm2Ijo6Gvb09goKCcOjQIUyZMgWpqami\nwGBlZYWhQ4ciJCQEJiYmWLNmDQoLC0U35rxMnTp1MHz4cMyZMweXLl2Ch4cHTExMcPPmTRw+fBh9\n+vSBh4cHZsyYATMzM7i6uqJmzZq4fPkyUlJS4OPjAwDYsmULjh49Cl9fX9SqVQv5+flYvXo1bG1t\nhRtsXtS6dWu4u7tj5syZyMvLQ4MGDZCWlobt27dj5MiRohClbTTwTYWGhmLfvn1Ys2YNpk2bhnr1\n6qF27dpYsmQJDAwMYGhoiA0bNohunFKrX78+Dhw4AB8fH1SrVg02NjawsbHBjBkzEBISguLiYnTs\n2BE1atRAbm4uTp48idq1a2Pw4MFaazEwMMC4ceMwc+ZMhIeHo1u3brh16xaWLl0KBwcHUQB+E337\n9kXfvn1f267s/n7dsT1//jzmzZuHTp064eOPP0ZpaSl27NgBIyMjeHl5AQAGDx6MPXv24IsvvsDg\nwYPh4OCAwsJCXLp0CceOHRM9xkwXY8aMgUKhQKNGjVC9enWcPXsWBw8eFKZN6Hpea/PHH38gMjIS\nXbt2RdOmTVGtWjVcv34d3333HR48eIAhQ4a8sjZHR0d06tQJ33///UvDbvfu3bFkyRJs2LABjRo1\n0givaWlp2Lp1K9q2bQt7e3s8fvwY3377LczNzUUfAomISD8Ydl9DJpNhwoQJWl9T3/w1Y8YMja8X\njYyMRPfu3TF16lSsWrVKWN68eXN4eHggNjYWt2/fRv369bF27Vp8/PHHGuvVZuLEiahXrx42bdqE\nTZs2QSaToVatWmjRooXQR9OmTbFjxw6kpqbi4cOHsLGxQY8ePYS74xUKBQ4ePIjY2Fjk5eWhevXq\naNasGWJiYkRfXFG2BplMhtWrVyM2NhZr167FvXv3YGdnh6lTp2LgwIE61a4rbe9Xh5Jt27Zh1KhR\nsLKyQnx8PObMmYOIiAhUr14dvXv3Rq1atYSpDmozZszA3LlzMXr0aBQXFyMkJARjx46Fr68vNm7c\niBUrVmD69OkoKiqClZUVXF1dXzrnWq1Pnz4wNTXFunXrEBISgipVqsDPzw+TJk3SmAOm6/548XFv\nurYp+/fXHVtra2vY2dlh/fr1uH37NkxMTODo6IjVq1cLU0fMzc2xZcsWLF++HGvXrsXt27dRrVo1\nODg46DwHtWxNHh4e+Omnn7Bp0yYUFRWhVq1aGDFiBEaNGiW00eW81sbV1RU9evTA//t//w87d+7E\n/fv3Ua1aNTg5OSExMVEjJGvbv+PHj8dPP/2k8TxdNVtbW3h5eeHIkSNavzHt448/hqmpKVasWIGc\nnByYmZnByckJCQkJevtiFiIi+h+Z6m0Mw5FW/v7+aNasGaKiovRdChHp2fXr1xEQEIBff/1V40kj\nJE3qy+bvyzSGz43+/nhXckkJALxRH+p+zGxs9DqNobzr5pxd/fo7/3by0WNEREREJFmcxvAO6XKZ\nmoiIiN5fHNH98DDsvkO6PhaLiIiIiCoGpzEQERERkWQx7BIRERGRZDHsEhEREZFkMewSERERkWQx\n7BIRERHpSC6XC8/apQ8Dwy4RERERSRbDLhERERFJFsMuEREREUkWwy4RERERSRbDLhERERFJFr8u\nmIiIiEhHWVlZ+i6Byokju0REREQkWQy7RERERCRZDLtEREREJFmcs0tERPQOeHt767sEKoPH45+D\nYZeIiOgdGDp0qL5LoDJ4PP45OI2BiIiISEdyuRxyuVzfZVA5MOwSERERkWQx7BIRERGRZDHsEhER\nEZFkMewSERERkWQx7BIRERGRZPHRY0REREQ6ysrK0ncJVE4c2SUiIiIiyWLYJSIiIiLJYtglIiIi\nIsli2CUiIiIiyWLYJSIiIiLJYtglIiIi0pFcLodcLtd3GVQODLtEREREJFkMu0REREQkWQy7RERE\nRCRZDLtEREREJFkMu0REREQkWUb6LoCIiIjoQ5GVlaXvEqicGHaJiIj+YR4DSC4peaP34w37UPdj\n9kY9EL0ewy4REdE/iJWV1Rv3oSooAACYmZu/UT9mFVQP0asw7BIREf2DREVF6bsEoneKN6gRERER\nkWQx7BIRERGRZDHsEhEREelILpdDLpfruwwqB4ZdIiIiIpIshl0iIiIikiyGXSIiIiKSLIZdIiIi\nIpIshl0iIiIikix+qQQRERGRjrKysvRdApUTR3aJiIiISLIYdomIiIhIshh2iYiIiEiyGHaJiIiI\nSLIYdomIiIhIshh2iYiIiHQkl8shl8v1XQaVA8MuEREREUkWwy4RERERSRbDLhERERFJFsMuERER\nEUkWwy4RERERSZaRvgsgIiL6p5s8eTJyc3MrrL+CggIAgLm5eYX0Z2VlhaioqArp60OXlZWl7xKo\nnBh2iYiI9Cw3Nxd37uTAsJJphfRX+qQQAPCktOL6IvpQMewSERG9BwwrmcLGvVuF9HXneCoAVEh/\n6r6IPlScs0tEREREksWwS0RERESSxbBLRERERJLFsEtERESkI7lcDrlcru8yqBwYdomIiIhIshh2\niYiIiEiyGHaJiIiISLIYdomIiIhIshh2iYiIiEiy+A1qRERERDrKysrSdwlUThzZJSIiIiLJYtgl\nIiIiIsli2CUiIiIiyWLYJSIiIiLJYtglIiIiIsli2CUiIiLSkVwuh1wu13cZVA4Mu0REREQkWQy7\nRERERCRZDLtEREREJFkMu0RE9I+QkJCAhIQEfZdBFYDHksqDYZeIiP4R0tPTkZ6eru8yqALwWFJ5\nGOm7ACIiIqIPRVZWlr5LoHLiyC4RERERSRbDLhERERFJFsMuEREREUkWwy4RERERSRbDLhERERFJ\nFsMuERERkY7kcjnkcrm+y6ByYNglIiIiIsli2CUiIiIiyWLYJSIiIiLJYtglIiIiIsli2CUiIiIi\nyTLSdwFEREREH4qsrCx9l0DlxJFdIiIiIpKs14bdnTt3QqFQaP3j4eHxLmrUG6VSiYYNG77z9QYF\nBYn2c9OmTdG/f3/s37//b/epVCrxxx9/VGCVuissLER4eDhatmwJhUKB+fPnv7Rt2e1u3LgxAgIC\nMHXqVNy+ffut13njxg0oFArs2rVLWDZ16lQEBASUq5+MjAwolcqKLg/Lli2DQqF4bTv1+RMREaHx\nWnJyMhQKBbKzsyu8vrfh4cOHUCqV+PPPPzVeCwoKwsCBA/VQFRERfUh0msYgk8kQFxcHW1tb0XJD\nQ8O3UtT74vPPP0fr1q31sm6FQoHZs2cDALKzs7Fy5UqMGzcOmzZtgrOzc7n7UyqVGD16NDw9PSu6\n1NfauHEj9uzZg8jISMjlclhbW7+yfe/evdG3b1+UlJTgzz//RFxcHDIzM5GSkgITE5N3VPVzY8aM\nwaBBg8r1noyMDCxfvhxjxoyBgUHFXTyRyWSQyWQ6t929ezdGjBiBevXqabz2oXjw4AGUSiU++ugj\njQ+es2bN0k9RRET0QdF5zq5CoUCdOnXeZi2vVVxc/E7Djq2trUbAf1fMzMyEUOvs7Aw3Nzf4+flh\nx44dfyvs6tNff/0FGxsbdOvWTaf2NjY2wjY2bdoUZmZmmDp1Kn7//Xe0bdtW63ve1rnxd855lUol\n+q8+NGzYEHfu3MHSpUsRFxentzre1Kv24YshnoiISJsKGXZSqVQICgqCv78/CgoKhOXnz5+Hi4sL\nFi1aJCzz9/dHeHg4kpOT0b59ezg7O6NXr14al9gjIiLg6+uLzMxM9OvXDy4uLoiOjhZe37p1K7p3\n7w5nZ2d4eXnhq6++wv3790V9bNiwAZ06dYKLiws8PDzQu3dv7Nu3T3j94MGD6NevH5o1awY3Nzd0\n6NAB8fHxwuvaLhsXFBRg9uzZ8PHxgZOTEzp06ID169eL2mRkZEChUGD//v2YM2cOvLy84OXlhfDw\ncNH+KQ9bW1tYWlri5s2bouWHDh1CcHAwvL294erqiq5duyIxMRHPnj0T2igUCshkMqxYsQIKhQIN\nGzYUXWbPyMjA4MGD0bRpU7i5uWHYsGG4cOGCTnWlpKSIjsPkyZORk5MjWveuXbuQnZ0trPvo0aPl\n2nYnJyeoVCpcuXIFwP+Oy4ULFzBs2DC4ublh4sSJQvu9e/eib9++cHV1RfPmzTF+/HiN/VZUVIRZ\ns2bB09MTbm5uGDNmDG7duqWx7oiICPj7+4uWFRYWIjo6Gu3atYOTkxO8vb0xbtw45OfnQ6lUYvny\n5QCAxo0bC9tcdr2LFi1CQEAAmjRpgoCAAKxcuVIj1J07dw5ffPEFnJ2d4evri/j4+HKFZ1NTU4wc\nORJ79+7FuXPnXttel31WUlKC2NhY+Pv7o0mTJvD398eSJUtQUlIiards2TL06tUL7u7u8PLywqBB\ng3Dq1ClRG11+Rm7cuIG2bdtCJpNh2rRpwr5UTzPRNo3h7Nmz+OKLL+Di4oI2bdpg1apViIuL0/g5\nLi0txargVVswAAAgAElEQVRVq9CxY0c4OTnBx8cHCxcuRHFxsdBGPa1l69atiIuLg7e3N5o3b45R\no0ZpTKvZvXs3evbsCTc3N7i7u6Nr165ISkp67X4nIqK3T+eR3dLSUpSWloqWGRgYCJdWo6Oj0a1b\nN8ycORMxMTF48uQJwsLC4OjoKAoiwPNfdOfOnUNYWBiMjY2xZs0aBAcHIyUlRfi+aZlMhocPHyIs\nLAzDhg3Dl19+iUqVKgEAoqOjsX79egwcOBBTpkzB7du3ERsbi4sXL2LLli2QyWRITU1FVFQUxo4d\nC3d3dxQVFeH8+fNCIL527RrGjBmDjh07YuzYsTA2NsaVK1dw7do1oc4XLxurVCoEBwfjzz//xPjx\n4+Ho6Ii0tDQsWLAAd+/e1djOyMhI+Pn5YfHixbh8+TKioqJgZGT0yjmrL1NQUIB79+6hbt26ouXX\nrl2Dp6cnAgMDYWpqijNnzkCpVOLu3bsICwsDACQlJaFPnz7o1asX+vXrBwDCiHVaWhpCQkLQpk0b\n4cPE6tWrERgYiN27d79yZHvr1q2YOXMmOnfujC+//BJ37tzB4sWLcfr0aezcuROmpqZISkpCXFwc\nzp8/L4TA8o7IXb16FQBQrVo1AP+7DB8SEoLPPvsMwcHBwnSBzZs34+uvv8Znn32GkJAQPHr0CMuW\nLUNQUBBSU1NRpUoVAMD06dPx008/ITQ0FE2aNMGhQ4cwadIkjUv8L54DT58+xZAhQ3D+/HmMHDkS\nLi4uePjwIdLT03H//n18/vnnuHXrFrZv344tW7aIpjGUlpZi6NChuHTpEkJCQtCgQQOcOnUKy5cv\nx/379zFlyhQAwN27dzFo0CDY2NggKioKxsbGWLduXbnn2fbr1w+JiYmIjY3FmjVrXtpO1302ZcoU\n/Pzzzxg1ahSaNm2KEydOYOXKlbh+/brog+jt27cxcOBA1K5dG4WFhUhNTcWAAQOwY8cONGjQQLTu\nV/2MWFtbQ6lUYuzYsRg1apTwoeNlo+13797F4MGD8dFHHwn9rF+/Hjdu3NA4rpMmTUJaWhqCg4Ph\n6uqKS5cuYcmSJbhx44bGSPjq1avh5uaG+fPnIy8vDwsWLEB4eDi++eYbAMCxY8cwefJkDBo0CJMn\nT4ZKpcKlS5fw4MEDHY8UEX1I1DmFT2X4cOgUdlUqFTp06KCx3M/PDytXrgTwPDzNmzcPoaGhaNWq\nFU6ePIlbt25h165dMDISryY/Px9JSUlCkPLy8oK/vz9WrFiBhQsXCu0KCwsRExODNm3aCMtu3LiB\nhIQEhIaGYvTo0cJyuVwu3MQVEBCAU6dO4ZNPPhG1KTv/9ty5cygpKcHMmTNhZmYGAK+dz5qWloYT\nJ05gwYIF6NGjBwCgZcuWePz4MRITEzFkyBBYWFgI7Zs3b45p06YJ7S5duoRt27bpHHbVHy6ys7Ox\naNEiWFhYaMwfVYdXNXd3dxQXFyMxMVEIu+opAba2thpTICIjI+Hp6Ska6fX09ERAQAASEhIwdepU\nrbU9e/YMcXFx8PLyQkxMjLDcwcEBgYGB2L59OwYMGABnZ2fUqFEDJiYmOk+/UKlUwoerc+fOISoq\nCqampvDz8xPayGQyDBw4EAMGDBCWPX78GDExMfjss88wd+5cYbmzszM+/fRTbNu2DQMHDsTly5fx\nww8/ICwsDMOHDwfw/Pg8evQIW7dufWVtKSkpOHXqFFasWCGqp3379sL/f/TRR8J6y4bd3bt34+TJ\nk/juu+/g7u4O4Pm5r1KpsHz5cowYMQKWlpZYv349ioqKkJCQIPyMtGzZUvRzoAtjY2OMGTMG06dP\nx/Hjx4V1lqXrPvvvf/+LH374AaGhoQgJCRFqMjQ0RFxcHIKDg+Ho6AgAon6ePXsGb29vnD17FsnJ\nyfj3v/8tWv+rfkZMTEyEUXF7e/vXnj+JiYl48uQJ1q1bBxsbGwCAt7e3xsj8sWPHsGfPHkRFRQlT\na1q0aIFq1aph8uTJ+M9//iMaCba3txeF+fz8fCxatAg5OTmwtrbG6dOnUa1aNdENgS1btnxlrURE\n9O7ofIPa8uXLNUb51CNtam3btkXfvn0xa9YsPH36FPPnz9c6CuPq6irqy8zMTJiyICrOyEgUKADg\n8OHDUKlU6NKli2ik2cnJCWZmZjh27BgCAgLg5OSEzZs3Y+7cuQgICICbmxsqV64stG/YsCGMjIww\nceJE9O7dG82bN4elpeUr98OxY8dgaGiILl26iJZ369YN27dvR2ZmpqheX19fUTtHR0cUFxcjLy8P\nNWvWfOW6jh8/jsaNGwt/r1SpEhISEmBvby9ql5OTg2XLliE9PR137twRLinLZLLXrufKlSu4evUq\nRo0aJdqXlSpVgqur6yunG1y+fBl5eXkao9nu7u6oXbs2MjIyREG0PFatWiV8iJLJZPjkk0+wZs0a\njRvbXpy/m5mZiUePHmmcG7a2tvjXv/6FY8eOYeDAgTh16pTWD3CdO3fGli1bXlnb4cOHYWVlpXFe\n6iI9PR21a9eGq6urqL6WLVtiyZIlOHXqFNq0aYPMzEyNnxFTU1O0adNG9KQIXfTq1Qtr165FbGws\nvvvuO43Xdd1nR48ehUwm05h33a1bNyxduhRHjx4Vwu7hw4excuVK0ZUUmUyGy5cva6z/TX5GXnTq\n1Cm4uLgIQRd4fi77+vpi586dwrKDBw/CxMQEn376qWibW7VqBZVKhWPHjonC7os3qaq38+bNm7C2\ntoaTkxMePHiA8PBwdO7cGe7u7qhatWq5aqd3o6CgAEVFRRg6dKi+S9GQm5sLlez9fBros5Ji5Obm\nvlf7LTc3V/Q7nehVdJ7G0KBBA51u1unRowe2bNkCKysrdO7cWWsbbb/EatasqTEPztLSUuPyY15e\nHlQqFdq1a6fRh0wmw71794Q6iouLsW3bNmzevBmGhobw9fVFREQE7OzsULduXaxbtw5r1qzBlClT\n8OTJEzg7O2PSpElo3ry51rrv37+P6tWra4xUW1lZQaVSCetWq169uujv6huonjx5orX/sho2bIh5\n8+ahpKQEFy9eRHR0NMaPH4/du3ejRo0aAJ6PgI4aNQq5ubkIDQ2Fg4MDKleujF9++QWrVq167Xry\n8vIAAF999ZXGiJtMJkPt2rVf+l71tmp7soK1tbXG/Ony6N27N/r37w9DQ0PUqlVLYz+WXU9Z6nNj\n8ODBGm1lMpkw6q6eU2xlZSVqo0u4unfv3t++aTE/Px83btwQfYgpW596n+bk5AiBqqwX69WFgYEB\nxo0bhy+//BIHDx7UeF3Xfaa+JP/iPlf/XV372bNnERwcjNatWyMyMhLW1tYwNDTEV199pfV8fJOf\nkRfput/y8/NRXFwMFxcXjbZlj4OuNTZv3hxLly7Ft99+i7FjxwrLIiIi8Mknn5R7O4iIqGJV6Deo\nFRUVYerUqXB0dMSVK1cQHR2t9TK4OmS9uOzFEKHtEUkWFhaQyWRISEjQGFlWv67Wp08f9OnTR5hT\nuWDBAoSFhQmXqj08PODh4YGnT5/ixIkTWLp0KUaOHIn9+/eL+lGrXr067t+/j5KSElHgzc3N1Vj3\nm6pSpQoaNWoE4PklZTs7OwwaNAhKpRLTp08H8Hwu69mzZxEdHS0abf711191Woe63rCwMK2XXY2N\njV/7XvW2l5WTk4MmTZroVIM21tbWWgPhi148P9Q1LVy4EPXr19dor56uog5oubm5opFybefli2rU\nqKHzzXsvsrCwQJ06dbB06VKtN5vZ2dkJ9WmrRdu+1kWnTp2wZs0aLFmyRGPai677TB34cnJyRB96\n1R8c1P3s3bsXxsbGUCqVoikcDx480PrzWpFett/K3jCprrVy5crYtGmT1uNQdmRYV+3bt0f79u1R\nWFiIjIwMLFq0CCNGjMDvv/9e7r7o7TE3N4e5uTkSEhL0XYqGoUOHIu/+37uB+W0zMDJBzerv1357\nn0aZ6f1XoddM5s6di5ycHMTHx2PSpEn45ptvcOjQIY12mZmZolHcgoICHDhwAG5ubq9dR6tWrWBg\nYIDs7Gw0btxY4486MJRVtWpVdOzYER07dsR///tfjdeNjY3h6emJ4cOHo7CwENevX9e6bg8PD5SW\nluKnn34SLU9NTYWJiQlcXV2FZRX9LFNPT0+0a9cOycnJwr4rLCwEIH7e8dOnT7F7926N9xsbG6Oo\nqEi07F//+hfs7Oxw8eJFrftS2yiZmoODA6ysrPDjjz+Klp84cQLZ2dl6eZ6vm5sbzMzMcOXKFa3b\no76pwMXFBTKZDHv27BG9//vvv3/tcWvVqhVyc3ORlpb20jbqkb8X97ePjw9u3rwJU1NTrfWpA6Or\nq6vGz8jjx4/x22+/6borNEyYMAFnz57Fzz//LFqu6z5r3rw5VCqVxvFOTU2FTCYTjndRUZHGs4WP\nHDmi9eY6XX5GyjPSq22/FRUVaQROHx8fPHnyBA8ePNC6za97DvSrmJqawtfXF3379kVOTg7u3r37\nt/siIqKKofMNaufOnUN+fr7Ga05OTjAwMMDPP/+Mbdu2ITo6Gvb29ggKCsKhQ4cwZcoUpKamiubD\nWllZYejQoQgJCYGJiQnWrFmDwsJC0c1kL1OnTh0MHz4cc+bMwaVLl+Dh4QETExPcvHkThw8fRp8+\nfeDh4YEZM2bAzMwMrq6uqFmzJi5fvoyUlBT4+PgAALZs2YKjR4/C19cXtWrVQn5+PlavXg1bW9uX\nhrzWrVvD3d0dM2fORF5eHho0aIC0tDRs374dI0eOFI3svo1nrIaGhmLfvn1Ys2YNpk2bhnr16qF2\n7dpYsmQJDAwMYGhoiA0bNmj9IoP69evjwIED8PHxQbVq1WBjYwMbGxvMmDEDISEhKC4uRseOHVGj\nRg3k5ubi5MmTqF27ttbL28D/Lo/PnDkT4eHh6NatG27duoWlS5fCwcEBvXv3rvDtfx1zc3NMnjwZ\nc+bMQV5eHlq3bo2qVavi9u3bOHr0KDw9PdG5c2c4ODigS5cuiIuLw7Nnz+Dk5IT09HStl/lf1K1b\nNyQlJSEsLAzBwcFwcXFBQUEB0tPTMXjwYDg4OAhPm0hISEDr1q1hYGCAJk2aoGvXrtixYwcGDRqE\noUOH4pNPPsHTp09x9epV/Pbbb4iPj0elSpUwePBgbN68GUOGDMHYsWNhYmKCdevWoXLlyn97eoiv\nry+aNm2K9PR0UcjUdZ81aNAAnTt3xrJly/D06VPR0xi6dOkijAr7+Pjgm2++wZQpU9CrVy9cvnwZ\nK1asEG7aK0uXnxErKytYWFjghx9+gKOjI0xNTWFvb6/1Kop6vw0bNgwhISEwNjbGhg0bUKlSJdE2\ne3h4oFOnThg/fjwGDRok3Eh4/fp1/P777wgPD8fHH3/8yrrK1h4XF4fc3Fx4eXnBxsYGN2/exLff\nfouGDRsKU46ISDr4FIYPj843qE2YMEHra0eOHEFRURFmzJiB7t27iy6nR0ZGonv37pg6dSpWrVol\nLG/evDk8PDwQGxuL27dvo379+li7dq3GL5iXjfxMnDgR9erVw6ZNm7Bp0ybIZDLUqlULLVq0EPpo\n2rQpduzYgdTUVDx8+BA2Njbo0aOHMKdOoVDg4MGDiI2NRV5eHqpXr45mzZohJiZG9OUEZWuQyWRY\nvXo1YmNjsXbtWty7dw92dnaYOnWqxvM+33RkV9v7HR0d0alTJ2zbtg2jRo2ClZUV4uPjMWfOHERE\nRKB69ero3bs3atWqJUx1UJsxYwbmzp2L0aNHo7i4GCEhIRg7dix8fX2xceNGrFixAtOnT0dRURGs\nrKzg6ur60jnXan369IGpqSnWrVuHkJAQVKlSBX5+fpg0aZLGjQPl+eYvXdq+rE3fvn1Rq1YtrFu3\nDj/88ANKS0thY2ODZs2aiW46mjNnDszMzJCQkICnT5+iRYsWiImJwRdffPHKdRkZGSExMRFKpRJJ\nSUlYvnw5LCws0LRpU+FSf5s2bfDFF19g8+bNwvNx//zzTxgZGWHdunVYvXo1kpKScP36dZiamqJu\n3brw8/MTpo3UqFEDGzZswLx58zB16lRYWFigX79+KC0tFR7f9nf2z8SJEzFw4ECN13TdZwsXLkTd\nunWxY8cOrFy5EjY2NggODhaezgA8f/rBtGnTkJiYiF9++QUNGjRAVFQUVqxYofWxbrpsx7x58xAb\nG4shQ4agtLQU8+fPF56GUrYP9X6bO3cuIiIihP2Wn5+P1NRUUb8xMTH49ttvsX37dqxatQomJiaw\ns7ODt7e3aO72y2osu9zFxQXffvst5s+fj/v376NmzZrCs5eJiEj/ZKp3/DVP/v7+aNasGaKiot7l\naonoH+jZs2fo2bMnLC0tkZiYqO9yRK5fv46AgAD8+uuvGk9ZobdDPc/zfZp7qqaes2vjrts3Tb7O\nnePPP+BVRH93jqe+t3N236ea6N34O/92VugNakRE+rR06VJ8/PHHqF27Nu7evYvk5GT897//feWX\nahARkbS987Cr62VqIqLykslkiI+Px507d4RnNMfHx8Pb21vfpRERkZ6887Cr62OxiIjKa9y4cZwr\nS0REIu/n17UQERERvYfkcrnwWEb6MDDsEhEREZFkMewSERERkWQx7BIRERGRZDHsEhEREZFkMewS\nERERkWTxSyWIiIiIdJSVlaXvEqicOLJLRERERJLFsEtEREREksWwS0RERESSxbBLRERERJLFsEtE\nREREksWwS0RERKQjuVwOuVyu7zKoHBh2iYiIiEiy+JxdIiL6R/D29tZ3CVRBeCypPBh2iYjoH2Ho\n0KH6LoEqCI8llQenMRARERGRZDHsEhEREZFkcRoDERERkY6ysrL0XQKVE0d2iYiIiEiyGHaJiIiI\nSLIYdomIiIhIshh2iYiIiEiyGHaJiIiISLIYdomIiIh0JJfLIZfL9V0GlQPDLhERERFJFsMuERER\nEUkWwy4RERERSRbDLhERERFJFsMuEREREUmWkb4LICIiIvpQZGVl6bsEKieO7BIRERGRZDHsEhER\nEZFkMewSERERkWQx7BIRERGRZPEGNSIiovdA6ZNC3DmeWmF9AaiQ/p73Zf7G/RDpC8MuERGRnllZ\nWVVofwUFz/9rbl4RIdW8wuv7kMnlcgB8KsOHhGGXiIhIz6KiovRdApFkcc4uEREREUkWwy4RERER\nSRbDLhERERFJFsMuEREREUkWb1AjIiIi0hGfwvDh4cguEREREUkWwy4RERERSRbDLhERERFJFsMu\nEREREUkWwy4RERERSRbDLhEREZGO5HI55HK5vsugcmDYJSIiIiLJYtglIiIiIsli2CUiIiIiyWLY\nJSIiIiLJYtglIiIiIsky0ncBRERERB+KrKwsfZdA5cSRXSIiIiKSLIZdIiIiIpIsTmMgIiKSuMmT\nJyM3N/etrqOgoAAAYG5u/tbWYWVlhaioqLfWP0kTwy4REZHE5ebm4k7OHRiYvr1f+88KSwAARSh+\nq/0TlRfDLhER0T+AgakRLDt+/Nb6z99zBQDe2jrU/ROVF+fsEhEREelILpdDLpfruwwqB4ZdIiIi\nIpIshl0iIiIikiyGXSIiIiKSLIZdIiIiIpIshl0iIiIikiw+eoyIiIhIR1lZWfougcqJI7tERERE\nJFkMu0REREQkWQy7RERERCRZDLtEREREJFkMu0REREQkWQy7RERERDqSy+WQy+X6LoPKgWGXiIiI\niCSLYZeIiIiIJIthl4iIiIgki2GXiIiIiCSLYZeIiIiIJMtI3wUQERERfSiysrL0XQKVE0d2iYiI\niEiyGHaJiIhekJCQgISEBH2XQR8QnjPvL4ZdIiKiF6SnpyM9PV3fZdAHhOfM+4thl4iIiIgki2GX\niIiIiCSLYZeIiIhIR3K5HHK5XN9lUDkw7BIRERGRZDHsEhEREZFkMewSERERkWQx7BIRERGRZDHs\nEhEREZFkGem7ACIiIqIPRVZWlr5LoHLiyC4RERERSRbDLhERERFJFsMuEREREUkWwy4RERERSRbD\nLhERERFJFsPuO7Rz504oFAqtfzw8PPRd3lulVCrRsGHDd77eoKAg0X5u2rQp+vfvj/379//tPpVK\nJf74448KrFJ3hYWFCA8PR8uWLaFQKDB//vyXtn3ZudawYUP8+uuv77BqTRV5Ply7dg0RERFo27Yt\nnJyc0LJlS/Tr1w9Lly4VtfP394dCocCyZcs0+oiNjYVCoQAA5Ofno0mTJpg9e/ZL15mcnAyFQoGj\nR49WyDYQ0YdDLpdDLpfruwwqBz567B2TyWSIi4uDra2taLmhoaGeKno3Pv/8c7Ru3Vov61YoFEJw\nyc7OxsqVKzFu3Dhs2rQJzs7O5e5PqVRi9OjR8PT0rOhSX2vjxo3Ys2cPIiMjIZfLYW1t/cr2vXv3\nRt++fTWWOzg4vK0SdVJR50N2djZ69+4NOzs7jB07FnZ2dsjNzcXp06exd+9ejB8/XtReJpNhw4YN\nCAoKgoWFhWi5TCYDAFhaWqJ169b48ccf8e9//xtGRpr/TKakpMDOzg7Nmzd/420gIqK3i2FXDxQK\nBerUqaPXGoqLi2FiYvLO1mdra6sR8N8VMzMzIdQ6OzvDzc0Nfn5+2LFjx98Ku/r0119/wcbGBt26\nddOpvY2NzXu5jRV1PiQnJ+Px48fYsGEDqlWrJizv2LEjpkyZotG+WbNmOH36NFatWqX1dbWePXvi\nt99+Q1paGtq2bSt67caNGzh+/DjGjBnzxvUTEdHbx2kM7xmVSoWgoCD4+/ujoKBAWH7+/Hm4uLhg\n0aJFwjJ/f3+Eh4cjOTkZ7du3h7OzM3r16qVxiT0iIgK+vr7IzMxEv3794OLigujoaOH1rVu3onv3\n7nB2doaXlxe++uor3L9/X9THhg0b0KlTJ7i4uMDDwwO9e/fGvn37hNcPHjyIfv36oVmzZnBzc0OH\nDh0QHx8vvL5s2TLhMrFaQUEBZs+eDR8fHzg5OaFDhw5Yv369qE1GRgYUCgX279+POXPmwMvLC15e\nXggPDxftn/KwtbWFpaUlbt68KVp+6NAhBAcHw9vbG66urujatSsSExPx7NkzoY1CoYBMJsOKFSuE\nKQFKpVJU7+DBg9G0aVO4ublh2LBhuHDhgk51paSkiI7D5MmTkZOTI1r3rl27kJ2dLaz7TS+jb9u2\nDQqFQjSt4dmzZxgwYADat2+PR48eCct///134fxp1qwZQkJCcPnyZVF/QUFB+OKLL3DkyBH06tVL\n2I9lzxVA+/lQWlqK1atXo3PnznB2dkaLFi0wYsQIjXWU9eDBA1SqVAlVq1bVaXtr1aqF/v37Y/Pm\nzbhz585L2/n5+aFatWpITU3VeG3Xrl0AgO7duwvLdu/ejZ49e8LNzQ3u7u7o2rUrkpKSdKqJiIje\nLoZdPSgtLdX4o1KpADy/nBodHY1Hjx5h5syZAIAnT54gLCwMjo6OmDhxoqivjIwMrF+/HmFhYYiN\njYWJiQmCg4NF3/Aik8nw8OFDhIWFoWvXrli7di26dOkCAIiOjsacOXPQqlUrrFy5ElOmTMHBgwcR\nHBws1JSamoqoqCh07doVa9asQXR0NDp06CAE4mvXrmHMmDGoW7culixZgpUrV2Lo0KEoLCwU1aC+\nTAw8D/XBwcHYuXMnhg0bhlWrVqF169ZYsGABYmNjNfZZZGQkZDIZFi9ejNDQUOzduxfz5s37W/u/\noKAA9+7dQ926dUXLr127Bk9PT8ybNw+rV69Gz549oVQqsWTJEqFNUlISVCoVevXqhaSkJGzduhWf\nf/45ACAtLQ1DhgyBubk5oqOjERMTg0ePHiEwMBC3b99+ZU1bt27FlClTUL9+fSiVSkyaNAnp6ekI\nCgoS9mNSUhJatWoFa2trYd2NGjV6Zb8qlUrr+ab22WefoUOHDpg2bZoQ/pYvX47MzEzExMTAzMwM\nwPOgO2rUKJiZmWHp0qX4+uuvceHCBQQGBmqExqtXryIyMhJDhw6FUqmEtbU1JkyYgGvXrgltXjwf\nAGDixImIi4uDn58f4uPjMXfuXNSvX18U+F/k5OSER48eYfz48Th27BiKi4tfuT8AYOTIkTAwMBB9\nGHuRsbExunTpgrS0NDx48ED02u7du+Hm5iacP8eOHcPkyZPh6emJ+Ph4LFu2DH379tV4HxER6Qen\nMbxjKpUKHTp00Fju5+eHlStXAng+8jhv3jyEhoaiVatWOHnyJG7duoVdu3ZpzB/Mz89HUlKScEnY\ny8sL/v7+WLFiBRYuXCi0KywsRExMDNq0aSMsu3HjBhISEhAaGorRo0cLy+VyuXATV0BAAE6dOoVP\nPvlE1KbsfMtz586hpKQEM2fOFMLR6+azpqWl4cSJE1iwYAF69OgBAGjZsiUeP36MxMREDBkyRDSn\nsnnz5pg2bZrQ7tKlS9i2bdsrb9AqSx3wsrOzsWjRIlhYWGDQoEGiNv369RP93d3dHcXFxUhMTERY\nWBgACFMCbG1tNaYHREZGwtPTUzTS6+npiYCAACQkJGDq1Klaa3v27Bni4uLg5eWFmJgYYbmDgwMC\nAwOxfft2DBgwAM7OzqhRowZMTEx0npqwatUq4bxSk8lkOHLkiLB/58yZg+7du2Py5MkICQnBypUr\nMWHCBDg5OQnvWbJkCerUqYM1a9bAwOD5Z2QXFxd06NABiYmJoikB9+7dw+bNm4WpOo0aNYK3tzf2\n7NmD4OBgrXUeOXIEe/fuxfTp0xEYGCgsDwgIeOX29ejRA5mZmUhKSsLevXthbGwMZ2dntG3bFoGB\ngVqn6tSoUQODBg3C2rVrMWzYsJdOKerRowc2btyIH3/8UTg3MjMzkZWVhWHDhgntTp8+jWrVqiEi\nIkJY1rJly1fWTURE7w7D7jsmk8mwfPlyjfmKZecbAkDbtm3Rt29fzJo1C0+fPsX8+fO1/lJ2dXUV\n9WVmZiZMWSjLyMgIfn5+omWHDx+GSqVCly5dRKN9Tk5OMDMzw7FjxxAQEAAnJyds3rwZc+fORUBA\nAHNePjYAACAASURBVNzc3FC5cmWhfcOGDWFkZISJEyeid+/eaN68OSwtLV+5H44dOwZDQ0NhhFmt\nW7du2L59OzIzM0X1+vr6ito5OjqiuLgYeXl5qFmz5ivXdfz4cTRu3Fj4e6VKlZCQkAB7e3tRu5yc\nHCxbtgzp6em4c+cOSkpKADw/Zq9bz5UrV3D16lWMGjVKtC8rVaoEV1fXV043uHz5MvLy8jRG7d3d\n3VG7dm1kZGRgwIABr9zGl+nduzf69++vsbzs+Va1alVER0cjKCgIw4YNg4eHB0aMGCG8XlhYiD//\n/BOjRo0Sgi4A2Nvbw83NDRkZGaK+5XK56Fy1tLSEpaUlsrOzX1rn4cOHYWBgIIySl8esWbMwYsQI\npKWl4eTJk/jjjz+wcOFC7Nq1C8nJyVoD77Bhw7Bp0yYsW7YMUVFRWvt1cnJCvXr1kJKSIoTdXbt2\noVKlSujYsaOo3YMHDxAeHo7O/7+9e4+rKd3/AP5ZoaJUSEZhcm27dBndyURkxIzLMJnRySV+1OQy\nh0wa1LgMczATSWeSW+O4DEaDwZhxvwxHaDJoHDIyEQrdKCnr94fXXmfv9i5767Kzz+f9evV67Z71\nrPV812XXdz/redYeNAjOzs4aD6uguquwsBDFxcUICgrSdSjVIicnB88NRF2HUSXPS8qQk5Oj83Pi\n4+MDACpx5OTkKP1vpLqDya4OdOzYUaMJakOHDsXWrVthaWmJQYMGqa2jLgFr1qyZym3zpk2bqtw2\nfvDgAURRhK+vr8o2BEFAbm6uFEdJSQl27NiBLVu2oF69evD29sasWbNgY2ODNm3aYO3atUhISEB4\neDiePn0KBwcHhIWFVThbPS8vD+bm5io91ZaWlhBFUWpbztzcXOl3eQLz9OlTtdtX1LlzZ3zxxRco\nLS3F9evXsWzZMkybNg179uxBkyZNALzocQ8ODkZOTg6mTJmCtm3bwtjYGL/88gvi4+Nf2s6DBw8A\nALNnz8Znn32mtEwQBFhbW1e4rnxf1T1ZoXnz5irjp7XRvHlzpUS/Ik5OTmjbti3S09NVEuv8/HyI\nolhhfBcvXlQqK3+ugBfnq7JjmJubC3Nz81eeNGljY4OAgAAEBARAFEV89dVXWLt2LbZv367UUyxn\namqKCRMmYPny5Zg0aVKF2x06dCi+/vpr/PXXX3jjjTewf/9+9OvXD6amplIdV1dXrFixAhs3bsTk\nyZOlslmzZsHOzu6V9oeIiKoPk906qri4GBEREejUqRMyMjKwbNkytbfB5UlW+bLyPcflE10AsLCw\ngCAIWLdunUrPsny5nL+/P/z9/VFQUICTJ0/iyy+/xPTp0/Hdd98BANzc3ODm5oZnz57hwoULWLFi\nBSZNmoTDhw8rbUfO3NwceXl5KC0tVUp4c3JyVNquqkaNGkljWx0cHGBjY4MxY8YgNjYWc+fOBfBi\nnOnly5exbNkypd5mTZ9HK493+vTpam9hN2jQ4KXryvddUXZ2Nrp166ZRDFWxcuVKZGRkwM7OThqO\nIU/ozMzMIAhChfGpS2611aRJE+Tl5VXLU0IEQUBwcDDWrFmD9PT0CusFBgYiMTER0dHR6NChg9o6\ngwcPRnR0NHbt2gWZTIa8vDyliWly/fv3R//+/VFUVISzZ89i6dKl+L//+z8cP368SvtCumNqagpT\nU1OsW7dO16FUi6CgIOQUPtR1GFViYFgPlqZN6+w50XWPM1WME9TqqIULFyI7OxtxcXEICwvDt99+\ni1OnTqnU++2335R6cQsLC3Hs2DG89dZbL22jZ8+eMDAwwJ07d9C1a1eVHxsbG5V1GjduDD8/P/j5\n+eE///mPyvIGDRrA3d0dEyZMQFFRETIzM9W27ebmhrKyMvz0009K5bt374ahoSGcnJykMnWJelW4\nu7vD19cX27dvl46dfBKY4vOOnz17hj179qis36BBAxQXFyuVtWvXDjY2Nrh+/braY9mpU6cK42nb\nti0sLS2xb98+pfILFy7gzp07Nf4833PnziE+Ph7Tp0/HN998g4KCAnz++efS8oYNG6Jr16746aef\npEmLwIsx3ykpKdUSX8+ePfH8+XNs375dq/Uqmrx248YNAOp7y+WMjY0REhKCgwcP4vfff1dbp0WL\nFvD09MSuXbvwww8/wNLSEl5eXhVus2HDhvD29sbIkSORnZ2NR48eabE3RERUE9izW8tEUcSVK1fw\n8KHqJ2x7e3sYGBjgwIED2LFjB5YtW4ZWrVohMDAQp06dQnh4OHbv3q00HtbS0hJBQUEIDQ2FoaEh\nEhISUFRUpDSZrCKtW7fGhAkTsGDBAty4cQNubm4wNDREVlYWfv31V/j7+8PNzQ2RkZEwMTGBk5MT\nmjVrhj///BO7du1Cr169AABbt25FcnIyvL290bJlSzx8+BCrV69GixYtKkzy3n77bTg7OyMqKgoP\nHjxAx44dcfToUXz//feYNGmSUs+uYoJVXaZMmYKDBw8iISEBc+bMQfv27WFtbY3ly5fDwMAA9erV\nQ2JiotIYVbkOHTrg2LFj6NWrF8zMzGBlZQUrKytERkYiNDQUJSUl8PPzQ5MmTZCTk4OUlBRYW1tj\n7NixamMxMDDA1KlTERUVhZkzZ2Lw4MG4e/cuVqxYgbZt22L48OGvvJ/37t1DamqqSrm1tbU0RCIs\nLAw9evTAuHHjAADz58/HJ598Ai8vL2ny4LRp0xAcHIyJEydi1KhRePz4MVauXAlzc3Npvapwd3dH\n//79sXjxYty5cwceHh4oLS1FcnIy+vTpU+FwmG+++QYpKSkYOHCgNHb8jz/+wNq1a9G0aVO8//77\nlbbr7++PdevW4dSpUxV+qBo2bBjCwsKQmZmJcePGqVwTMTExyMnJgYeHB6ysrJCVlYWNGzeic+fO\n0jAZIiLSHSa7tUwQBHzyySdql50+fRrFxcWIjIzEkCFDlG6nL1q0CEOGDEFERATi4+OlcldXV7i5\nuSE6Ohr37t1Dhw4dsGbNGrz55psq7arz97//He3bt8fmzZuxefNmCIKAli1bwtPTU9pG9+7dsXPn\nTuzevRsFBQWwsrLC0KFDpfGJMpkMJ06cQHR0NB48eABzc3O4uLjgq6++UrolrRiDIAhYvXo1oqOj\nsWbNGuTm5sLGxgYREREYPXq0RrFrSt36nTp1wsCBA7Fjxw4EBwfD0tIScXFxWLBgAWbNmgVzc3MM\nHz4cLVu2lIY6yEVGRmLhwoUICQlBSUkJQkNDMXnyZHh7e2PTpk345z//iblz56K4uBiWlpZwcnKq\ncMy1nL+/Pxo2bIi1a9ciNDQUjRo1Qu/evREWFqYy4UHT4yEIApKSkpCUlKSy7NNPP8W4ceMQGRmJ\nZ8+eKT25Y8CAARgxYgQWLFgAZ2dntG7dGr169UJ8fDxiY2Px97//XerBDwsLU+k9VRefukeNlf99\n+fLlSEhIQFJSEr799ls0btwY9vb28Pf3r3AfhwwZgrKyMuzatQurV6/GkydPYGVlBS8vL4SEhCgN\n51EXQ4MGDTBlyhRERERUeFx9fX1hamqKJ0+eqB3C4OjoiI0bN2Lx4sXIy8tDs2bN4OXlhalTp1YY\nNxER1R5BrIluM6oVPj4+cHFxqXA2ORHVXZmZmejbty8OHTqk8mQQ0j35+Mu6Oj5UW/Ixu0393nx5\n5Vf0cH8GANRYGw/3Z9SJMbu2trYAoPQ8e0D/rpm66lX+dnLMLhERERHpLSa7rzF1t2WJiIiI6L84\nZvc1puljsYiIiIj+V7Fnl4iIiIj0FpNdIiIiItJbHMZAREREpKHyT2Gguo89u0RERESkt5jsEhER\nEZHeYrJLRERERHqLyS4RERER6S0mu0RERESkt5jsEhEREWnI1tYWtra2ug6DtMBkl4iIiIj0FpNd\nIiIiItJbTHaJiIiISG8x2SUiIiIivcWvCyYiIirHy8tL1yHQa4bXTN3FZJeIiKicoKAgXYdAddTN\nmzfVlvOaqbs4jIGIiIiI9BaTXSIiIiLSW0x2iYiIiEhvMdklIiIiIr3FZJeIiIiI9BaTXSIiIiIN\n2drawtbWVtdhkBaY7BIRERGR3mKyS0RERER6i8kuEREREektJrtEREREpLeY7BIRERGR3qqv6wCI\niIiIXhc3b97UdQikJfbsEhEREZHeYrJLRERERHqLyS4RERER6S0mu0RERESkt5jsEhEREZHeYrJL\nREREpCFbW1vY2trqOgzSApNdIiIiItJbTHaJiIiISG/xSyWIiIj+BzwvKsXD/Rk1un0ANdbG86JS\nwLRGNk16jskuERGRnrO0tKzxNgpRCAAwNa2hjNS0dvaD9A+TXSIiIj23ZMkSXYdApDNMdomIiIg0\ndPPmTV2HQFriBDUiIiIi0ltMdomIiIhIbzHZJSIiIiK9xWSXiIiIiPQWk10iIiIi0ltMdomIiIg0\nZGtrC1tbW12HQVpgsktEREREeovP2SUi0oGysjIAwN27d3UcCRG9iszMTF2H8D9J/jdT/jdUE0x2\niYh0IDs7GwAQEBCg40iISBtGRkYAgL59++o4kv9t2dnZePPNNzWqK4iiKNZwPEREVE5xcTEuXbqE\n5s2bo169eroOh4jotVBWVobs7Gx069YNxsbGGq3DZJeIiIiI9BYnqBERERGR3mKyS0RERER6i8ku\nEREREektJrtEREREpLeY7BIR1bL169cjODgYXl5ekMlkiI2NrbDutm3b4OfnB3t7ewwYMABbt26t\nxUg1l5ubi4ULF6Jfv35wdHRE3759sWDBAjx8+FDXoWns3r17iIiIgJeXF+zt7dG3b19ER0frOiyt\n7d27FzKZDL1799Z1KC918+ZNLFiwAIMGDcJbb70FLy8vhISE4I8//tB1aCru3r2LqVOnwsXFBc7O\nzpgyZQqysrJ0HZZGfvrpJ4SGhqJ3795wdHTEgAED8PXXX+Px48e6Du2VjR8/HjKZDCtWrHhpXT5n\nl4iolm3fvh2NGzeGr69vpcnrtm3bEBUVheDgYHh6euL06dOYN28eAODDDz+srXA1EhwcjFu3bmHa\ntGlo164drl+/jhUrVuDy5ct1NkFXdPv2bXz00Udo3bo15syZA0tLS2RmZuLWrVu6Dk0rBQUFWLx4\nMZo3b67rUDRy6tQpnD17FsOHD0fXrl1RUFCANWvWYOTIkdiyZQu6dOmi6xABvHhU4OjRo2FkZIQl\nS5YAAKKjozFmzBjs3r1b40dg6cr69evRokULzJgxA2+88QbS0tKwcuVKnD179rV4f5b3448/4urV\nqxAEQbMVRCIi0onS0lLRzs5OXLlypdplnp6e4qxZs5TKIyIiRA8PD7G0tLS2wnypP//8U7SzsxO3\nbdumVL5lyxZRJpOJf/75p24C00JQUJD4wQcfiGVlZboOpUrmzJkjjh8/Xpw1a5bo7e2t63Be6tGj\nRyplBQUFoqurqxgeHq6DiNTbsGGD2KVLF/HWrVtS2V9//SV26dJFXL9+ve4C09DDhw9VypKSkkSZ\nTCaeOXNGBxG9utzcXLFnz57i3r17RTs7O3H58uUvXYfDGIiI6qCUlBQ8evQIgwcPViofMmQIcnNz\ncf78eR1FpurZs2cAAFNTU6Xyxo0bAwCeP39e6zFp46+//sKpU6cQGBgIA4PX99/i+fPn8eOPPyIq\nKkrXoWjMwsJCpczU1BS2tra4d++eDiJS78iRI3B0dETr1q2lslatWqF79+44dOiQDiPTTJMmTVTK\n7O3tIYpinTrOmli2bBns7OwwcOBAjdd5fd/VRER67Pr16wCAjh07KpV37NgRoihKy+uCjh07wtXV\nFXFxcbh06RKePHmCixcvIi4uDt7e3mjXrp2uQ6zU+fPnIQgCDA0NERQUBHt7e7i5uSE8PBy5ubm6\nDk8jpaWliIqKwvjx45USstdRXl4erl27hvbt2+s6FMn169dV3osA0KFDB6Snp+sgoqo7e/YsBEGo\nU8f5Zc6dO4fdu3cjMjJSq/WY7BIR1UF5eXkAADMzM6Vyc3NzpeV1xerVq2Fra4sRI0age/fu8Pf3\nR+vWrRETE6Pr0F7q/v37EEURs2fPRtu2bbFmzRrMnDkTR48exYQJE3QdnkZWr16NZ8+eYeLEiboO\npcrmz58PABgzZoyOI/mv3Nxc6b2nyNzcHPn5+TqIqGru3buHlStXokePHujatauuw9HIs2fP8Pnn\nn2P8+PF48803tVqXE9SIiKrg9OnTGDdu3Evrubm54dtvv62FiKruVfZpzpw5SE1NxYIFC9C2bVvc\nuHEDMTExmDJlCuLj42s6ZCXaxi+KIgDA3d0dc+fOlV6bmJhgxowZOHHiBHr16lWjMSvSNv6MjAzE\nx8cjLi4OhoaGtRBhxar6foiPj8e+ffuwaNGi176Huq568uQJQkJC0KBBAyxatEjX4WgsISEBT58+\nRXBwsNbrMtklIqqC7t27Y//+/S+t17BhQ622K+/Rzc/Ph6WlpVQu79FV18tUXbTdp6NHj2Lv3r1I\nTEyEu7s7AMDFxQWtWrVCUFAQDh8+DB8fnxqLtzxt45ePG+3Ro4fSci8vL4iiiLS0tFpNdrWNf+HC\nhfD09ISDgwMKCgogiiJKSkogiiIKCgpgaGgIIyOjmg4bQNXeD1u2bEF0dDSmT5+OYcOG1UR4r8zc\n3Fzt3ZS8vDyVuy912dOnTzFp0iTcvn0bmzZtQosWLXQdkkaysrIQHx+PL774Ak+fPsXTp0+lD6kl\nJSUoKCiAiYlJhWPumewSEVWBkZER2rZtW+3blY/NvXbtmlKyKx+r26FDh2pvU07bffrPf/4DQRBU\nboc6ODgAANLT02s12dU2/po8lq9C2/jT09ORlZUFV1dXlWVubm4YPXo0IiIiqjPECr3q++GHH37A\n/PnzMX78+Do5FKNDhw5qx8lfv379tRnzWlpaiilTpuDKlStYv359nbvuK/PXX3+hpKQEM2fOlJJc\nABAEAWvXrsW6deuQlJQEmUymdn0mu0REdZCTkxOaNGmCPXv2wNPTUyrftWsXLCws0L17dx1Gp0z+\nTNdLly7Bw8NDKv/tt98AoM73Hjk5OcHS0hInTpxAQECAVH78+HEIggB7e3sdRvdyy5cvx9OnT5XK\n4uPjceXKFcTExNT54//LL79g9uzZ8Pf3x8yZM3Udjlo+Pj5YunQpMjMz0apVKwBAZmYmUlJSEBYW\npuPoXk4URcyYMQNnz55FfHy89EH0ddGlSxe1w14CAwMxZMgQfPDBB5WO42WyS0RUyy5duoTbt2+j\nrKwMwIueuQMHDgAAevfuDSMjI9SvXx/Tpk3D/PnzYWVlhR49euD06dNISkrC3LlzUb9+3fnz7evr\ni+joaHz66acICQlBu3btkJ6ejri4OFhbW8PX11fXIVaqXr16CAsLQ0REBKKiotC/f3/cvHkTK1as\ngLu7u9KHjbpIXeKyc+dOGBoawsXFRQcRaS45ORkzZsyATCbD0KFDkZqaKi0zNDRE586ddRjdf/n7\n+2Pz5s34+OOPMW3aNABATEwMrK2tMXLkSB1H93Kff/45Dhw4gJCQEBgbGysd5zfeeKPOfyAyNTVV\ne+cCAKytrV96nQuiYn8wERHVuIiICPzwww9qlx06dAjW1tbS79u2bcO6detw584dtGzZEuPGjatz\n354G/Hd295kzZ5CdnY3mzZujR48emDx5MqysrHQdnkZ2796NhIQEZGRkwNzcHAMGDMD06dO1Hm9d\nF0RERODMmTM4cuSIrkOpVGxsLFatWqV2mbW1dZ16hu3du3exaNEi/PrrrxBFET169EBERITS+7Wu\n8vHxqfCrjUNDQzF58uRajqh6dO7cGSEhIZg6dWql9ZjsEhEREZHe4nN2iYiIiEhvMdklIiIiIr3F\nZJeIiIiI9BaTXSIiIiLSW0x2iYiIiEhvMdklIiIiIr3FZJeIiIiI9BaTXSKiOiQiIgIymQwymQx3\n7typtXajoqIgk8kQGBgolZ09e1aKZfTo0SrrBAYGSsuTk5PVlst/nJyc8N577yE2Nlblq21jYmIg\nk8kwePBgrWLesGEDBgwYAEdHR8hksgq/YakmlN8/mUwGR0dHDBo0CDExMSguLq61WF5HlV1X+kTx\n/SyTybB+/XqVOpMmTVKqU9EXzlTV2bNnERsbi9jY2Cr9bVH8uxAbGyuVK77vFcnbTEpKeuU2q4rJ\nLhFRHSQIQq21lZ6ejh07dkAQBHz88cdax6JuuSAISj9Pnz7FtWvXEBsbi5kzZyrVHTNmDBo2bIhr\n167h+++/1yjmEydO4Msvv0RGRgZKSkogCAIMDGr3X1r5fSwpKZG+JnnMmDHS10GTKvn5qs3rXJfk\n18jWrVuVyu/cuYMTJ04oXUc1RZ7srlq1Crdv367y9srHKo+//PuQyS4REenc+vXrUVZWBhsbG3h6\nelbLNuVfzrlx40ZcvnwZq1evRr169QAAv/zyC+7fvy/VNTc3xzvvvANRFLFu3TqNtn/p0iXp9eLF\ni5GWloZ///vf1RI7AJSUlLy0jnwf09LSkJaWhu+++w4WFhYQBAEXL17Ejz/+WOn6ZWVleP78ebXE\nqy1N9q8mpaWl4cqVK0hMTNRpHFWlzTkURRG3bt3C6dOnpbKtW7dK69fUF9rW1rn+9ttvpfNanq4/\n1DDZJSJ6DRQVFSEmJgbvvvsuHB0d4eTkhGHDhmHDhg0qPYg5OTmYNm0aunfvDnd3d0RGRuLIkSPS\nLcaIiAip7pMnT7B3714IgoCBAwdWe9yiKMLAwAC9evWCnZ2dVJ6VlaVUT972jRs3cP78+Uq36ePj\ngxUrVkj/QGfNmqUyBOPQoUMYN24c3Nzc0K1bN/Tp0wezZ89W6dGS33rt3Lkzrl27hqCgILz11luY\nMGGC1vvq4OCAd999V0pafv/9dwBAUlKSdOy3bt2KL7/8El5eXujWrRvu3r0LALh9+zZmz56NPn36\noFu3bnB1dcXYsWNx+PBhlXZSU1Px4YcfwsHBAT4+Pli/fj1iY2PV3gb38fGBTCZD3759ce7cOXz4\n4YdwdHREVFSUVGfPnj0ICAiAi4sL7O3t8c477yA6OlplKMbPP/+MgIAAeHp6wt7eHl5eXvjb3/6m\ndGv+0aNHmDdvHvr16wcnJyc4OztjwIABmDFjBm7evCnVq2gYw9WrVzF9+nTp+Hh4eCA4OBjnzp1T\nqqe4v4cOHcK8efPQs2dPuLi4YPz48cjIyNDonGl63DU5hy9jY2MDURSxZcsWAEBpaSl27twJQRBg\nY2NT4XpVvZbHjx8PHx8fxMbGQhAEiKKodghSVFQU3n//fXh6eqJbt27o3r07PvjgAynelyk/jGHn\nzp2QyWRSm4rDHwIDA5GYmCj9vn//fqVtTZ06FTKZDN26dcO9e/c0ar8y9au8BSIiqlFFRUUICAjA\nlStXlHpI/vjjD6SlpeH06dOIj48H8KIXZ+zYsbh+/ToEQUBRURG2b9+Oo0ePqu1dOX/+PIqKiiAI\nApydnWt0PxR7rpo2baq0rHv37jAwMIAoijhx4kSlsZS/3St/Lb99Gh8fj+joaKVld+/exffff4+D\nBw9i8+bNaN++vcr6gYGByMvLUyqryj6qi3v58uUqbVy/fh2jRo1Cfn6+VFZYWIgzZ87gzJkzmD59\nOiZOnAjgxYeBsWPHSoloVlYWlixZgubNm1cYtyAIePjwIcaPHy/18snrLViwAJs2bVJa79atW4iP\nj8evv/6KTZs2wdDQEBcvXsQnn3yitH8PHjzAgwcPUFxcjHHjxgEAwsPDcfz4caXtZWRkICMjA4MH\nD4atra1SXIrOnDmDiRMnSsNSACAvLw9Hjx7F8ePHsWTJErz77rsq+xYREYGCggKp7NSpUwgJCZE+\nxFUkPT0dH330kUbHXbE9dedQEyNHjsTXX3+Nw4cPIzs7G8nJycjJyUGzZs3g6+urdjxvdV3LFb1n\nFMuSkpLw7Nkz6feioiL8/vvv+P333/Hw4UOEhoZqtJ+K21bXJvDivTpixAjExMTgyZMn2Lp1K/z8\n/AAAjx8/xrFjxyAIAry8vNCiRQuN2q0Me3aJiOq4DRs2SIlur169cOrUKRw8eBCdO3cGABw/fhx7\n9+4FAPzwww9SotutWzccPXoUBw4cgKmpqdpETN77CEBlYokixV4ZdZPSKlNWVobjx4/j6tWrEAQB\nDg4OaN26tVIdU1NTqXfr4sWLlW7v0KFDCA0NVRoqkZaWhsTERNy5cwcrV66EIAgwNzfHv/71L5w7\ndw4hISEAgPz8fCxatEhpe/LttGrVCj/++CNSU1Px+eefa7RvittITU1VGrrg6OioUqeoqAhff/01\nUlJScODAATRt2hRffPGFlHCFhIQgOTkZ//rXv2BmZgZBEBATEyP1Hq5atQpFRUUAAH9/fyQnJ2Pd\nunXIz8+vNL7i4mK4ubnh0KFDSElJQXBwMFJTU6VEd9iwYTh16hR+++03aUz1pUuXsHnzZgAvPhTJ\nb7d/9913uHTpEo4dO4ZvvvlGKQE9d+4cBEGAr68vzp07h/Pnz2P37t0IDw9/adISFRWFZ8+eQRAE\nzJs3D+fPn0dsbCzq168PURSxYMECtRP/GjdujF27duHEiRNo164dAODPP/986XW0cOFCjY+7nLpz\n2KxZs0rbkbOzs4OTkxPKysrw3XffYcuWLRAEASNGjED9+qp9j9V1Lc+bN0/pPSMIgvSeuXLlijSx\nc/Hixfj5559x4cIFXLp0Cbt27cIbb7wB4MUQBW0NGzYMaWlpUpuurq7SkJ/ExESYmJhg+PDhUq+v\nvOf/4MGD0iRWf39/rdtVh8kuEVEdd+zYMen19OnT0bRpU9jY2Cj1tMjrnDlzRioLDg5GixYt0KZN\nG6nnrbycnBzpdZMmTSqMofxkLE0n0wQGBqJr166YOHEiRFGEm5sbYmJi1NZt0qQJRFFEdnb2S7er\nSDGJP3nyJEpLSwEAQ4YMgbOzM0xMTDB58mRp+//+97/VjmOcO3cu2rdvD0NDQ7Rt2/al7cpvY6xt\nFwAAChlJREFUz8pvHY8cORJ5eXkQBAH29vYqw0IEQcCQIUPg5+cHY2NjtG7dGoIg4OzZs1JCM3ny\nZJiamsLZ2RnDhg2DKIooKyvDyZMnAUBpXHJYWBhMTU3h6emJfv36vfT4LF68GNbW1jA2NkabNm2U\nbtXv3LkTPXr0gKOjI5YsWSKtd+rUKQAvkie5+Ph4JCYm4sqVK7C3t8fYsWOlZa1atYIoikhJSUFc\nXBwOHDiAkpISjBkzptIPUzdv3pSGHtjZ2cHf3x+NGjVC37590bt3b4iiiPz8fKSkpKisGxQUhE6d\nOsHS0hJvv/22VF7ZJKynT59qddzl1J1DIyOjCtsp76OPPoIoiti4cSOSk5NhYGCAkSNHqq1bk9ey\nug++giDgs88+Q+/evWFvb4/BgwdLyX5+fj4ePnyo8X5qKjAwULojI5+8J//gbmlpid69e1dLO0x2\niYjquEePHkmvW7ZsKb1WHOf34MEDlbrW1tZq13sVir0y8h8XF5eXrlc+MS4qKqrRSVmK/5AV99/A\nwEDqWSwrK5Nu8SqS95RrQ3H/jIyM0L59ewQHByMxMVGakKeoS5cuSr/n5uZKY66trKyUZrIrxi/f\nL/n5NTExQePGjdXWVadZs2awtLRUKlM8VhV9mJEfJ19fXwQEBMDQ0BCHDx/G0qVLERwcjF69emHB\nggXSdhYuXIh27dohJycH69atQ0REBIYPH46+ffvijz/+qDA+xVjKX6vqjoMieW8uADRq1Eh6XdnE\nLG2Pu6Ly51Abfn5+sLCwkHqUe/XqVeG5q81ree/evZg+fTrOnTuH/Px8qTdW8QNtTTxOr3Xr1vDx\n8YEoikhKSsK9e/fw66+/QhAEvP/++9X2hBUmu0REdZzi+FbFiV2Kz8qU30pV7J1VnNhRfkJY+fUA\n5US5umzcuBHnzp3D2LFjIYoiLl68iMmTJ6vtWXr06BEEQVBJyrShuD+Kx+f58+fS8ahXrx7Mzc1V\n1jU0NNSqrfJPY0hNTcXevXsxbdo0NGzYUO06xsbGSr9bWFhISfH9+/eVjoviOZNfA/Lz+/jxYzx+\n/FhtXXXU9T4qXldLly5V+TCTlpaGbdu2SXXmzp2L5ORkbNu2DUuXLoW3tzfKysqwefNmpKamAngx\nSW/fvn04dOgQEhISEBYWhkaNGiErKwvLli2rMD7F81Z+X9QdB0WKQwA0HUOr7XFXVP4casPQ0FDq\nOQZe9PRWpDav5X379kmv586di4sXLyItLe2VPgBqa8yYMQBe9B6HhYWhtLRUGt5RXZjsEhHVcYq3\n8qKjo/HgwQNkZmZi1apVKnUUHx2WkJCA+/fvIyMjQ+3kFwCwt7eXXlfW8/aqRFGEiYkJPv30U6lH\n7MqVK9ixY4dSvcLCQum2s2JM2vLy8pLGeO7atQvnz59HYWEhYmNjpWTa09NT62SgphgZGcHDwwOi\nKCIvLw8rV65EYWEhzp8/Lz2XtH79+vDy8gIAeHh4SOsuX74chYWFOH36NH755Ret2+7Tpw+AF+do\n+fLluHDhAkpKSpCXl4fjx49jxowZ2LNnDwAgOTkZCQkJuHHjBmxtbdG/f3+lMcnyZCw6OhpHjhyB\ngYEB3N3dMWDAAJibm0MUxUoT8jfffBO2trYQRRFXr17Ftm3b8OTJExw+fBhHjhwBAJiZmeGtt97S\nej/V0fa4V6dRo0ahX79+eO+99+Dt7V1hveq+lhU/CF+9elUpwVe8C2FiYoLnz5/j+++/R1pa2ivs\noTILCwuIoog7d+6oHVvu6uqKLl26QBRFJCcnS+N7y4/rrwomu0REddzo0aPRtWtXAC/G5vbs2RP9\n+vXD5cuXIQgCvL29pfGhQ4YMQceOHQG8mFT09ttv45133kFhYaG0PcXeL2dnZ6kXsqJHflXH8z8F\nQcCMGTOk7a1atUrpNvOFCxek4Q09e/Z85XZatmyJqVOnQhAE5OfnS4/UiouLk8Znzpo1q2o7U80+\n++wzqXcuLi4OLi4uCAgIkMb/Tps2TZoo9PHHH0vna+PGjXBxccG4ceNgZmamdbtOTk4YNWoUBEHA\n7du3MWrUKDg4OMDd3R0TJ07Evn37pFv9WVlZ+OqrrzB06FC4urrCwcFBGnvdsGFD6ekZ+/fvR0hI\niDTus1+/fsjKypJu2Ssqf13NmzdP6oGOjIxE9+7d8fHHH6O0tBT16tVDZGRklXpVy9PmuFen1q1b\nIzY2VhobXZHqvpYVP5wsXLgQnTt3lsZR+/r6SsvCw8Ph6OiIhQsXVsv+Ozk5AQAyMzPh5uam8s1r\nAKRH0Mn/NlXXxDQ5JrtERHVM+bFyDRs2xKZNmxAaGooOHTrAyMgIxsbG6NKlC2bNmoW4uDiprqGh\nIdavX4933nkHjRo1goWFBT788EN88sknUh0LCwvptYmJCfz8/CCKotKtzPKxVHR7uKJl6sp79uwJ\nT09PCIKAe/fuKc3wlrfdpk0buLu7v+wQVdr2xIkTERcXBw8PD5iZmaF+/fpo2bIlRowYgZ07dyo9\nqqmy7WjStqbrVVa3ffv2SEpKwogRI2BtbY369evDzMwMHh4eiIuLU3rmb7t27ZCYmAhHR0cYGRnB\nxsYG4eHhShPUyk80rKztyMhILF26FK6urjAzM0ODBg3QsmVLeHh44NNPP5UmfHXt2hXDhw9Hhw4d\npGPatGlT+Pj4YOPGjbCysgIA/O1vf4OnpydatGgBQ0NDGBsbo2PHjpg6darSN+epO37u7u7Yvn07\nBg4ciObNm6N+/fqwsLBAnz59sHHjRgwaNEij/dL0vGhz3LXZ7qvGo65edV7L3bp1w9y5c9GmTRs0\naNAAgvDfbzt777338Nlnn6FVq1YwNjaGg4MDEhISpEmU5bepzd+DOXPmoHfv3jA3N5faLF9n0KBB\nsLS0hCiKMDMzU0q+q4Mg1tRXdhARkU5cuHABtra20njD7OxsTJkyBb/99hsEQUBCQoLS7dn09HQM\nHjwYz58/x9q1a9GjR49ajTcvLw+9e/dGcXEx5s+fjw8++KBW23/dnDx5Em5ubtLt68uXLyMoKAh5\neXlo2LAhjh079ko9vUS6kpOTAz8/PxQWFmLs2LEIDw+v1u3zSyWIiPTMhg0b8PPPP8PCwgINGjTA\ngwcP8Pz5cwjCi29JKz8OsX379hgxYgS2bduGf/7zn7We7CYmJqK4uBgdO3as1kkp+iosLAwFBQVo\n1qwZSkpKkJubK/WohYeHM9Gl18bFixcxc+ZM3L9/H0VFRTA1Na3wMYlVwZ5dIiI9s3PnTmzbtg03\nb95EYWEhTExMYGdnh/fffx9Dhw7VdXhURf/4xz9w8uRJZGVlobi4GE2bNoWTkxNGjx6t0ePgiOqK\ns2fPYsyYMWjQoAE6deqEiIiIGvkmRya7RERERKS3OEGNiIiIiPQWk10iIiIi0ltMdomIiIhIbzHZ\nJSIiIiK9xWSXiIiIiPQWk10iIiIi0lv/D87v72sNHoUPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9b2ae9910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look at expression ratios, separately\n",
    "## on original scale \n",
    "coefdata = univariate_coefplot(univariate_models,\n",
    "                               metrics=[x for x in extra_cols if 'expression_ratio' in x],\n",
    "                               hue=None)\n",
    "plt.close()\n",
    "print('{{{univariate-ratios-pfs-original}}}')\n",
    "sb.boxplot(data=coefdata,\n",
    "           x='value',\n",
    "           y='metric',\n",
    "           fliersize=0, whis=[2.5, 97.5])\n",
    "_ = plt.vlines(x=0, ymin=-10, ymax=10, linestyles='--')\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['Expression Rate of Missense SNVs',\n",
    "                'Expression Rate of Predicted Neoantigens',\n",
    "                'Expression Rate of Exonic SNVs',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('log(HR) for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{compare_exonic_snv_count_pfs:median HR of 0.75 vs. 0.82 for expressed versus total}}}\n",
      "{{{compare_missense_snv_count_pfs:median HR of 0.73 vs. 0.81 for expressed versus total}}}\n",
      "{{{compare_neoantigen_count_pfs:median HR of 0.84 vs. 0.93 for expressed versus total}}}\n"
     ]
    }
   ],
   "source": [
    "univariate_pfs_coefdata = extract_univariate_coefdata(univariate_models,\n",
    "                                                 metrics=['missense_snv_count','exonic_snv_count','neoantigen_count']\n",
    "                                                 )\n",
    "\n",
    "\n",
    "coefdata_pfs_grouped = univariate_pfs_coefdata.groupby('metric')\n",
    "for name, group in coefdata_pfs_grouped:\n",
    "    hyper_label='compare_%s_pfs' % name\n",
    "    paper.hyper_label_printer(label=hyper_label, formatter=paper.compare_posterior_dist, df=group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analysis of OS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:27:06.509337",
     "start_time": "2016-08-17T08:24:59.367537"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 22.390 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 19.371 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 21.278 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 22.475 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 29.977 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 20.460 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 21.339 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 22.168 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 21.674 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 19.907 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 29.093 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 22.110 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 27.347 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 20.193 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 20.868 sec.\n"
     ]
    }
   ],
   "source": [
    "univariate_os_models = list()\n",
    "for var in vars_centered:\n",
    "    univariate_os_models.append(survstan_os(formula = var, model_cohort = 'univariate survival analysis for OS, unstructured hazard'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-metrics-os-all}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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GqN3ezc1NaSIKAErj162srFSen/giJlJiQlRPubm5Yfv27Th06JBSt7nExEQY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\nIrXFhIiIiIiIiNQWEyIiIiIiIlJb/w+J+yK435XtWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb988a02ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{univariate-metrics-os-all}}}')\n",
    "coefdata = univariate_coefplot(univariate_os_models,\n",
    "                              metrics=['missense_snv_count',\n",
    "                                       'exonic_snv_count',\n",
    "                                       'neoantigen_count',\n",
    "                                       'peripheral_clonality_a',\n",
    "                                       'tcell_fraction',\n",
    "                                      ])\n",
    "_ = plt.xlim([0, 2.5])\n",
    "_ = plt.yticks([0, 1, 2, 3, 4],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "                'log(Baseline Peripheral TCR Clonality)',\n",
    "                'log(TIL Fraction)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-metrics-os}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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+zeHDh/noo48YPHgw3333HQCurq4sWLCAjIwMTp48yZIlS3jrrbcICwujRo0a\nODo6Ur9+fYYMGZJtljHApAUqP4oUKcKYMWMYM2YMly5dYvfu3Xz88cfY2Ngwbtw4HB0dsbOz46uv\nvnokxwPo06cPCxYsyPMZNu3atWPZsmXMmzePnj175limZcuWODg4EBYWhqOjI6dPn2batGnZyvn4\n+ODj40NqairHjh1j/vz5DB06lH379mULclnCw8OpUqUKNWrUKNC5OTg40KJFC5YtW0a9evVMQlN+\nZR3zt99+MwkqN27cADC5CZVnQ3x8PHFxV7C0vWcMl4U1Bot7uqXeuRtZDNZ2xkV//Yol/c5tnJ2d\nCA0NzXYMFxcXAM38JSLynFFQKQAfHx9WrFjBrl27TLp/hYWFYWNjY/zm3sPDg9DQUGJjY41jBJKT\nk4039Fmsra2pWLFijoOjN27cCEBwcDDOzs4MHDiQixcvMnLkSJYvX57rTer9vPDCCwwdOpRffvmF\n9u3bP9A+ihQpgr+/P3/88QezZs3i2rVrJjegFhYWuLm5MWrUKMLDwzl79iw1atSgSZMmHD9+nBo1\najzyAdXlypWjf//+hIWFcfr0aeDucz6WL19OYmKicYB6fl29epUSJUpkm/wgLi6OP//8874h5623\n3mLo0KHs3r07x/U2Nja0bduWb775Bmtra2xsbPJ8P6ytrXn55ZcZNGgQw4cPJzo6Os+gkt9B9H/V\np08fUlJS7tvtKze//vorQLbujNHR0RgMhgcKP2L+LG2LUNq7Y67r4368O9tgfsqIiIhkUVApgKZN\nm+Lt7c20adO4evUqNWvWZP/+/WzcuJGhQ4cabxz79+/P2rVrGThwIMOHD8fa2prVq1dja2ub7ca3\nQYMGREZGZjvWSy+9REZGBqdOnWL27Nm8+OKLREZGUq1aNXx9fR/qPIKCggq8zYIFC4iPj6dhw4aU\nLl2aS5cusWbNGuPzQ/bv38+6deto2bIlFStW5NatW6xZswYHBwfj4OlRo0bRvXt3evfuzeuvv06F\nChW4ceMGp0+fJjo6mvfff79AderZsyctWrSgVq1a2Nvbc/jwYX799Vfj4HofHx/atWvH6NGj6dev\nH25ublhYWBAdHc13333HO++8k2vXt61bt7Ju3To6dOiAm5sbRYoU4fz586xcuRIbG5s8n3sDd8dv\neHl5ERERketDQ7t06cL69evZsGEDrVq1Mk4wkOUf//gHR44cwd/fn3LlypGQkMDSpUspU6ZMjl0L\n4W6rXWTvJD7mAAAgAElEQVRkpMmkBwXh7e2d6yQAf3Xt2jVOnDgB3A3iJ06c4LPPPqNOnTrZuqad\nOHGCMmXKULFixQeql4iIiDx/FFTu496bTIPBwNKlS5k7dy7Lly/n+vXrVKhQgUmTJvHGG28Yyzk5\nObF69WpmzpzJxIkTcXR0pGfPniQkJJg8xwTudsXaunUrFy9epHz58ibrjh8/zq1bt4ytAQcPHsw2\nIL+g55BXmbzKubu7s2bNGmbPns2NGzcoWbIkfn5+jBo1CoAqVapQpEgRPv30U65cuULRokWpV68e\noaGhxlalcuXKsXHjRhYtWsTcuXNJSEjA0dGRWrVqZRvUn1td7l3eoEEDdu3axbJly0hLS6NSpUpM\nnjyZPn36GMvMmTOHNWvWsHHjRpYsWYKNjQ0VKlTAz8+PkiVL5nq+zZo1Iy4ujn379vHFF1+QlJSE\nk5MT3t7efPLJJ9keuJlTfceMGcMbb7yR67l4enpSpUoVLly4YJzJ7F6urq4cPHiQuXPncvXqVYoX\nL079+vWZM2dOri1Se/fupWTJkiZTWd9Pfj8ffxUREUFERARwt4WofPny9OnTh8GDB2ebbnv//v0P\n3IInIiIizydDZk6d9+WRy8jIoEuXLpQoUYKVK1cal2dmZtKmTRu6du3KsGHDCrGG8iwYNGgQ5cuX\nz3HmrcJy4sQJevfuzY4dO+47eUN0dDQBAQGEh4er9eUpERgYyNUbSY+k61fJ4g4aoyLyAPS7U55V\nalF5TObPn0+VKlUoX748165dY8OGDfz222/ZHgRoMBgYOXIkH374IQMGDMDW1raQaizPguXLlxd2\nFbJZtmwZXbp0yfcMcyIiIiKgoPLYGAwGQkJCiIuLw2AwULt2bUJCQvDz88tWtkOHDsTFxREdHV2g\n56KImLs7d+5Qp04dXnvttcKuijzF1JIiIvJ8UlB5TEaNGmUcv5EfOT3NW+RpZ2try/Dhwwu7GiIi\nIvIU0pPpRURERETE7CioiIiIiIiI2VFQERERAEJDQ3Ocdetp8iycg4iI3KWgIiIigOmzcZ5Wz8I5\niIjIXQoqIiJi1lxcXIzPUhERkeeHgoqIiIiIiJgdBRURERERETE7CioiIiIiImJ2FFRERERERMTs\nKKiIiIiIiIjZsSrsCoiIiOQlKiqqsKsgIiKFQC0qIiIiIiJidhRURERERETE7Kjrl4iIAJCUlERy\ncjKBgYH53iY+Pp5Mw8N/55WRlkJ8fHyBjp1bfezs7B66PiIiUvjUoiIiIiIiImZHLSoiIgKAg4MD\nDg4OhIaG5nubwMBArt5IeuhjW1jZULJ4wY6dW31EROTZoBYVERExay4uLri4uBR2NURE5AlTUBER\nEREREbOjoCIiIiIiImZHQUVERERERMyOgoqIiIiIiJgdBRURERERETE7mp5YRETMWlRUVGFXQURE\nCoFaVERERERExOwoqIiIiIiIiNlR1y8REQHAz8+vsKvw0J6FcxARkbsUVEREBIDAwMDCrsJDexbO\nQURE7lLXLxERERERMTsKKiIiYtZcXFxwcXEp7GqIiMgTpqAiIiIiIiJmR0FFRERERETMjoKKiIiI\niIiYHQUVERERERExOwoqIiIiIiJidvQcFRERMWtRUVGFXQURESkECioiIvJQ0u/cJu7HsDzXA/ko\n4/CoqyYiIk8xBRUREXlgzs7O9y2TlHT3XweHvIKIQ772JSIizw8FFREReWDBwcGFXQUREXlGaTC9\niIiIiIiYHQUVERERERExOwoqIiJi1lxcXHBxcSnsaoiIyBOmoCIiIiIiImZHQUVERERERMyOgoqI\niIiIiJgdBRURERERETE7CioiIiIiImJ29MBHERExa1FRUYVdBRERKQRqUREREREREbOjoCIiIiIi\nImZHQUVERERERMyOgoqIiIiIiJgdBRURERERETE7CioiImLWXFxccHFxKexqiIjIE6agIiIiIiIi\nZkdBRUREREREzI6CioiIiIiImB0FFRERERERMTsKKiIiIiIiYnasCrsCIiLyfBk/fjzx8fG5rk9K\nSgLAwcEBgBYtWgAQGBhoLOPs7ExwcPBjrKWIiBQ2BRUREXmi4uPjuRIXh30u62//71/DrVs5rs95\nqYiIPGsUVERE5ImzB7pb5fwnaENaGuRjvYiIPNs0RkVERERERMyOgoqIiIiIiJgdBRURERERETE7\nCioiImLWgvbsIWjPnsKuhoiIPGEKKiIiIiIiYnYUVERERERExOwoqIiIiIiIiNlRUBEREREREbOj\noCIiIg8sNDSU0NDQwq5GgTyNdRYReR7pyfQiIvLAIiIiAAgMDHxsxwhp3fqR7u9J1FlERB6eWlRE\nRERERMTsKKiIiIiIiIjZUVARERERERGzo6AiIiIiIiJmR0FFRERERETMjoKKiIiYtaA9ewjas6ew\nqyEiIk+YgoqIiIiIiJgdBRURERERETE7CioiIiIiImJ29GR6ERF5YElJSSQnJxfoKe/x8fFYPsQx\nU4Db8fEP/GT5+Ph47OzsHqIGIiLyJKhFRUREREREzI5aVERE5IE5ODjg4OBAaGhovrcJDAzkZlxc\nvsuHtG5t8toGKOrsXKBj/vX4IiJi/tSiIiIiIiIiZkdBRUREREREzI6CioiIiIiImB0FFRERERER\nMTsKKiIiIiIiYnYUVERExKwF7dlD0J49hV0NERF5whRURERERETE7CioiIiIiIiI2VFQERERERER\ns6Mn04uIyAPz8/Mr7CoU2NNYZxGR55GCioiIPLDAwMDCrkKBPY11FhF5HimoiIiIWQtp3bqwqyAi\nIoVAY1RERERERMTs3DeoLFy4EFdX18dekdjYWDw9Pfn5559zLTNlyhQmTpwIwNWrV3F1deXs2bP3\n3XdMTAyurq7G/+rWrUubNm2YPXs2iYmJj+wccnP48GFcXV05cuSIcVnfvn154403CrSfvXv3smrV\nqkdcO5g4cSItWrS4b7kWLVrg6urKwoULs62bO3fuE/mcPCoxMTEsWrSI6OjobOtatGjBpEmTCqFW\nj9bx48dxdXUlNjY21zJZPxNz587NcX1AQACurq6MHz/euOyvP0+urq40bNiQvn37EhERYbL9L7/8\ngoeHB5cvX340JyUiIiLPjfsGFYPBgMFgeOwVmTdvHi+//DIvvfRSrmV+/vln4/qTJ09ib29P9erV\n832MYcOGsX79elatWkW3bt1Yt24dI0aMeOi658dfr+F7773HtGnTCrSP8PDwxxJUCvIeGwwGVq9e\nzfXr1x94H+YgK6hcuHAh27qQkBCCgoIKoVaPVnh4OHXr1qVMmTJ5lnNwcGDbtm3Zlh89epSLFy9i\nb2+f43ZZP0/r169n1qxZWFtbM2zYMCIjI41l6tSpg6+vL/Pnz3+4kxEREZHnjll0/bp69Srbtm2j\nd+/euZZJSUnhzJkz1K1bF7gbVOrUqVOg41SsWBE3Nzfq16/PkCFDGDx4MEeOHOGXX37J87iPQ/Xq\n1QsUssxF/fr1SU1NZcmSJYVdlYeSmZmZa7BydXWlUqVKT7hGj97evXtp2bLlfcu1bNmSy5cvm7T4\nAWzZsgUfHx+cnJxy3C7r58nNzY0WLVoQEhICwLfffmtSrkePHmzbto0rV6484JmIiIjI8+iBgkpS\nUhLTp0+nSZMm1KtXj1deeSXHb/p//vlnevfujbu7O82bN2fJkiUsWLAgWxehjRs34uDgkOeUkadO\nnSIzM9O47cmTJ/NsfcmPunXrkpmZye+//w7c7QLl7+/P8ePH6dmzJ+7u7nz88cfG8uvWraNTp064\nubnRsGFDpkyZwo0bN0z2mZCQwLhx4/D29qZBgwZMnDiRxMREMjMzTcrl1PUrISGB9957j2bNmlGv\nXj2aNWvGhAkTSElJYdKkSWzevJnY2Fhjd5uAgACTbadOnUrTpk2pV68ebdu2Zf369dnO+dChQ3Tt\n2hU3Nzdat27NunXrCnTNypUrR69evVi7di1xcXH3LZ+fa5afz1NKSgqzZ8+mQ4cOeHp64ufnx7Bh\nwzh37pxJuU2bNuHq6sqJEyd4++238fb2pkmTJsycOdMYOg8fPky/fv0AGDBgAK6urtSpU8d4o55T\n168ffviBLl264ObmRps2bdiwYUOOXeaSk5P56KOPCAgIoG7dugQEBPDZZ5+ZvP9ZXQH37dvHjBkz\naNiwIQ0bNuSdd94hKSnJZH+rV6+mXbt2uLu74+PjQ7du3di7d+99r/vZs2c5f/58voJKuXLl8PHx\nYevWrcZlKSkp7N69m86dO2f77ObG1tYWa2tr0tLSTJb7+flRtGhRNm/enK/9iIiIiMADzPqVmZnJ\nkCFD+OWXXxg9ejS1atVi//79fPDBB1y7do0xY8YAcO3aNfr370/ZsmUJDg7GysqKVatWERMTk+2b\n7IiICDw8PLCwMM1NMTExJjfjBoMBT09Pk9eff/45FSpUIDw8vKCnwoULFzAYDBQrVsy4vz///JOx\nY8cycOBAxo0bh62tLQAff/wxq1at4o033mDChAnExsYyd+5czpw5wz/+8Q/jOY0YMYLffvuNcePG\nUaVKFXbs2MHMmTPv2y0qMTGRnj17kpiYSFBQELVq1SIhIYHw8HDS0tIICgoiISGBkydP8umnnwJg\nY2MD3L3R79WrF6mpqYwaNYoKFSoQERHBe++9R2pqKn369AHu3rwOGTIENzc35s2bx507d1i4cCG3\nbt3C0tIy39dt6NChrF+/npCQEN57771cy+XnmuX385SSksLNmzcZNmwYpUuX5saNG6xdu5aePXuy\nc+dOSpYsaXwPAcaPH8+rr77KokWLOH78OAsXLsTR0ZERI0bw4osvMnXqVGbMmMG7775LvXr1AHJt\n4Tpz5gxDhw7Fw8ODefPmkZKSwuLFi0lKSjL5zKanpxMYGMi5c+cYPnw4NWvW5MSJEyxevJgbN24w\nYcIEk/3OmjWLZs2a8cknn3D+/Hnjz8ns2bMBCAsLIzg4mBEjRuDt7U1ycjK//vprtqCXk/DwcKpU\nqUKNGjXuWxagU6dOvP/++0ydOhUbGxu+/fZb0tLSaNOmTY5jkuDu74L09HTgbqvo8uXLSU1NpU2b\nNiblLC0t8fT05ODBgwwZMiRf9RG5V9CePYBm/xIRed4UOKjs37+fY8eO8cEHH9C5c2cAGjduzK1b\nt1i5ciUDBgzA0dGRlStXcufOHVasWEHp0qWBu9+s5jRoOzIykv79+2dbXrp0abZs2QLAzJkzqVSp\nEv369ePcuXOMGzeOL7/8Ent7e6ytrfNV96wbq9TUVH788Uc+++wzSpcuTf369Y1lbt++zZw5c2je\nvLlxWUxMDKGhoYwcOZI333zTuNzFxYVevXqxb98+AgIC+P777zl27Bhz586lbdu2APj6+jJ48OA8\nBzQDrFy5kpiYGDZu3GjS4tSuXTsA7O3tKVGiBNbW1ri5uZlsu3r1ai5fvsz27duNXZYaNWpEYmIi\nixYtolevXlhYWBASEoKDgwOhoaHGAObp6UnLli3vO47hXk5OTvTr14/ly5czcODAHLtJ5fea5ffz\n5ODgwMyZM437ycjIwM/Pj8aNG7N9+3ZjC0mWDh06GMcfNWrUiBMnTrB9+3ZGjBiBg4MDNWrUIDMz\nk2rVqmW7nn/16aef8sILL7BixQpjOPT29iYgIIBSpUoZy23bto2ffvqJL774Am9vbwAaNmxIZmYm\nixcvZvDgwZQoUcJYvkGDBvz97383nvO5c+f4+uuvjUHlxIkT1K5d2+T6NW3aNM+6ZgkPDzcJ+ffT\npk0bpk+fzt69e2nXrh1bt26lVatWuY5PAZg6dSrvvvuu8bWtrS3Tp0/Hw8MjW9k6deoQGhqa7/qI\niIiIFLjr19GjR7G0tOTVV181Wd6xY0dSUlI4fvw4cPcmy93d3RhS4O6NjL+/v8l2iYmJJCcnm9zA\nZbG2tjZ2czp37hzNmzfH1dWVmzdvUrlyZby8vHB1dc33WI+pU6fy0ksv4eHhwaBBg6hatSrLli0z\n3nwCWFlZ0axZM5PtfvjhBzIzM3n11VdJT083/levXj2KFi3K0aNHAfjpp5+wsrKi9V++9Wvfvv19\n6/bDDz9Qr169B5o5KyIiAjc3N8qXL29SP19fX65du8aZM2eAu++Jv7+/MaQAlC1bFi8vrwIfc+DA\ngdjb2+f6bXt+r9mRI0dy/TylpqYaP08AO3bsoEePHjRo0IAXX3wRDw8Pbt++zfnz5022NRgM2T5n\ntWrV4tKlSwU+T/i/63bv56RUqVImrXtw930oX748Hh4eJufcuHFjUlNTOXHihEn5nOqYkpLC1atX\nAahXrx6nTp1i5syZHDp0iOTk5HzV98qVK0RGRtKqVat8n6O9vT0tW7YkLCyM+Ph4vv/+e2NwzE1Q\nUBAbN25k48aNhIaG0r17d95991127NiRrWyJEiVISUnJNgmDPL9uARvS0nL879Zf1t8Ebt7z+lah\n1lxERJ6UAreo3Lhxg+LFi2NlZbqps7MzgPFG5MqVK9SqVSvb9lnlsty5cwfA5CYwS1a3kqioKBIS\nEnB3dyctLY0ff/wRNzc34/r8dlsKCgqiRYsW2NjYUK5cORwcHLKVKVGiRLZuWlevXiUzMzPHGz+D\nwWA85/j4eIoVK5atPlndkvJy/fr1B57eNyEhgT/++CPHMTv31u/KlSs51sXZ2ZmYmJgCHdPBwYFB\ngwYxb948hg4dmm19fq9ZYmJirp+nzMxMY7l9+/YxduxYunbtyogRI3BycsLCwoLBgwcbP0P3cnR0\nNHltY2PzwBMjXLlyJccg7ezsbDK9cUJCAjExMfd9H7IUL148Wx3h/34mOnfuTEpKCl9//TVr167F\n0tISf39/Jk6cSIUKFXKt7969eylZsmS2IHU/nTt3ZujQoaxatYqSJUvSqFGjPMuXK1fO5FwbN27M\nhQsXmDVrlrElMIudnZ3JucnzY/z48cTHx5ssi4+PJxO4bZHzd2WZGRkApFhb3/1Z/9/v1KL/++Kr\nKNn/loiIyLOnwEGlePHi3Lhxg7S0NJOby6w/RFkzBJUqVcr4zfC9/jrzT9YNZU7PM7n3Juiv35Ib\nDAa2bduGwWDIc9aue/31xionOY0lcXR0xGAwEBoaahzPktM5lCpVisTERNLT003CSk7X4a+cnJzu\n2z0sN46OjpQsWZK///3vOQ58rlq1qrF+OdXlrzcR+dW3b19Wr17N3Llzs42FyO81u9/nKavcjh07\nqFKlCrNmzTKWSUtLy9d4jdzkdzrlUqVKkZCQkG35X6+bo6MjlSpVYv78+Tm+D3mFi9z06NGDHj16\n8OeffxIREcEHH3zA2LFj85wEITw83KTrYn41btyYEiVKGLvcPch00zVq1OC7774jISHBJNxlhbTc\nZhCTZ1d8fDxxcVewtC1iXJaRkQkYMFjb5biNJZB+5zZOTk6Ehoayb98+AHUfFBF5zhQ4qPj4+LBi\nxQp27dpl0l0nLCwMGxsb3N3dAfDw8CA0NJTY2Fjj+Ifk5GS+++47k/1ZW1tTsWLFHJ9nsXHjRgCC\ng4NxdnZm4MCBXLx4kZEjR7J8+fJs35o/Lr6+vlhYWHDx4sU8v2X29PQkLS2N3bt3m3yjvH379nwd\n47PPPuPXX3+ldu3aOZaxtrbOsftPkyZN+OKLLyhbtmyO3/xn8fDw4MCBAyQnJxu/4b506RLHjh0r\n0BiVLHZ2drz55pvMmDGD27dvZzuf/Fyz+32essY7JCcnZ2t12bJli7FVraBsbGzIzMzM1zf87u7u\nHDhwgDt37hi7zcXFxXHs2DGTro1NmjRhz549FClSxBgOc1PQEPDCCy/Qtm1bTpw4kWdISUpK4t//\n/jeLFi0q0P6z6jR8+HAOHjxI165dC7w93J2dz9raOltrZXR0NOXKlcux5VSefZa2RSjt3dH4Ou7H\nMACTZX+VVUZERJ5fBQ4qTZs2xdvbm2nTpnH16lVq1qzJ/v372bhxI0OHDjWGh/79+7N27VoGDhzI\n8OHDsba2ZvXq1dja2ma7SWvQoIHJQ+KyvPTSS2RkZHDq1Clmz57Niy++SGRkJNWqVcPX1/cBT7ng\nKlWqxKBBg5gxYwbnzp3Dx8cHGxsbLl26xA8//ECPHj3w8fGhcePGxmtz7do146xfWWNE8tK/f3+2\nb9/OgAEDGDZsmHHWr3379jF9+nTs7e2pUaMGGzZsYO3atdStWxdbW1tq1apF//792blzJ71796Z/\n//5UrVqV27dvc+7cOY4ePWp8vsWbb77Jrl27GDBgAAMHDjTOXnXvgPCC6tGjB6GhoXz//fcm72t+\nr1l+P09NmjQhPDyc2bNn06xZM/7zn//w5ZdfZus+lV8uLi5YWVmxceNGihUrho2NDdWqVctx8HhQ\nUBC7d+8mMDCQwMBA7ty5w6effkqpUqVMZv3q0KEDmzZtol+/fgQGBlK7dm1SU1P5448/+Oc//0lI\nSIgx6ORnyt+pU6dStGhRPDw8KFmyJOfPn2fr1q00adIk120OHDiAjY3Nfbtt5ea1117jtddey1fZ\nCxcuGMfd3Lhxg/DwcH744Qf69OmTLZBERkaaTFohUhBRUVGFXQURESkE+Qoq996AGgwGli5dyty5\nc1m+fDnXr1+nQoUKTJo0yeS5IE5OTqxevZqZM2cyceJEHB0d6dmzJwkJCYSFmX5T1rZtW7Zu3crF\nixcpX768ybrjx49z69YtGjZsCMDBgwezDUIu6Dk8SLkxY8ZQvXp1vvrqK7766isMBgPlypWjUaNG\nVKlSxVhu0aJFzJw5k08++QRLS0sCAgKYOnUqw4cPz/NYL7zwAmvXrmXevHksW7aM69ev4+zsTMOG\nDY2zmnXv3p0TJ04wb948EhMTKV++POHh4Tg4OPCPf/yDxYsXs3z5cmJjYylWrBhVq1Y1GdhfvXp1\nli1bxkcffcTYsWMpU6YMgwcP5qeffuLw4cP5ujZ/vT7W1taMHDmSSZMmZVuXn2uW389Tjx49uHz5\nMhs3bmT9+vXUrVuXJUuWMHz48Ad6bx0dHZk6dSrLli3jjTfeID09nc8//5wGDRpkO8+s6xYcHMyY\nMWOM1+27777j4sWLxnJWVlasWLGCpUuXsn79eqKjoylSpAiVK1emWbNmJrPT5afOXl5ebNq0ibCw\nMP78809Kly5N586djbOZ5WTv3r34+fnlu+Uip/c0P2Wy3relS5cCd8csVapUiWnTptGjRw+Tspcv\nX+bUqVO89dZb+aqTiIiICIAhM79Pc3sEMjIy6NKli7EffJbMzEzatGlD165dGTZs2JOqjsgDu3Xr\nFq1bt6Z58+bMmDGjsKsDQGpqKo0bN2batGnZZlErTEuXLmXdunXs3bv3vqEoOjqagIAAwsPDqVix\n4hOqoTxOgYGBXL2R9EBdv0oWd9C4FJF80O9OeVYVuOtXQcyfP58qVapQvnx5rl27xoYNG/jtt99Y\ntmyZSTmDwcDIkSP58MMPGTBggMn0uSLmYObMmXh6elK6dGliY2P5/PPPSUxMpG/fvoVdNSNra2uO\nHDlS2NUwkZKSwpo1a3jnnXceaHC+iIiIPL8ea1AxGAyEhIQQFxeHwWCgdu3ahISE4Ofnl61shw4d\niIuLIzo6Ot/PRRF5Uu7cucOcOXOIj483PnRz1apVOU7BLf8nOjqafv360bFj7t+ci4iIiOTksQaV\nUaNGMWrUqHyXHzhw4GOsjciDM5fuXU+batWqUa1atcKuhoiIiDyFCvxkehERkSfJxcUFFxeXwq6G\niIg8YQoqIiIiIiJidhRURESkQEJDQ81+Nq6noY4iIpI3BRURESmQiIgIIiIiCrsaeXoa6igiInlT\nUBEREREREbOjoCIiIiIiImbnsU5PLCIi8rCioqIKuwoiIlII1KIiIiIiIiJmR0FFRERERETMjoKK\niIiIiIiYHQUVERERERExOwoqIiIiIiJidhRURETErLm4uODi4lLY1RARkSdM0xOLiEiBJCUlkZyc\nTGBg4H3LxsfHk2ko+HdiGWkpxMfHExgYSK1atQDydbx7j2tnZ1fg44qIiPlQi4qIiIiIiJgdtaiI\niEiBODg44ODgQGho6H3LBgYGcvVGUoGPYWFlQ8nid4+R1e1rz549+d6+IK0vIiJintSiIiIiIiIi\nZkdBRUREREREzI66fomIiFmLiooq7CqIiEghUIuKiIiIiIiYHQUVERERERExOwoqIiIiIiJidhRU\nRERERETE7CioiIiIiIiI2VFQERERs+bi4mJ86KOIiDw/ND2xiIgUiJ+fX2FX4b6ehjqKiEjeFFRE\nRKRAAgMDC7sK9/U01FFERPKmrl8iIiIiImJ2FFRERERERMTsKKiIiIiIiIjZ0RgVERExa1FRUYVd\nBRERKQRqUREREREREbOjoCIiIiIiImZHQUVERERERMyOgoqIiIiIiJgdDaYXEZHHKv3ObeJ+DDN5\nDZgsy2kbcHjcVRMRETOmoCIiIo+Ns7NztmVJSXf/dXDIK4g4GLd1cXEBNPuXiMjzRkFFREQem+Dg\n4MKugoiIPKU0RkVERERERMyOgoqIiIiIiJgdBRURERERETE7CioiIiIiImJ2NJheRETMmmb7EhF5\nPqlFRUREREREzI6CioiIiIiImB0FFRERERERMTsKKiIiIiIiYnYUVERERERExOwoqIiIiFlzcXHB\nxcWlsKshIiJPmIKKiIiIiIiYHQUVERERERExOwoqIiIiIiJidhRURERERETE7CioiIiIiIiI2bEq\n7AqIiIjkJSoqqrCrICIihUAtKiIiIiIiYnYUVERERERExOwoqIiIiIiIiNnRGBUREXksxo8fT3x8\nfJ5lkpKSAHBwcMi1jLOzM8HBwY+0biIiYv4UVERE5LGIj4/nSlwc9nmUuf2/fw23buW4PuelIiLy\nPFBQERGRx8Ye6G6V+5+aDWlpkEeZDWlpbNiwgX379mn2LxGR54zGqIiIiIiIiNlRUBEREREREbOj\noGafE7wAACAASURBVCIiIiIiImZHQUVERERERMyOgoqIiIiIiJgdBRURETFr3bt314xfIiLPIQUV\nERERERExOwoqIiIiIiJidhRUROT/t3fncVFV///AXyMCsiS4YSokKuqgrApoyCcVRdOSxQU1c6/U\nMMtd+5qmHz+4PDQR0dIEXErDlaXcgtzQxyd3UIGPiVIgiSKIIiLb/f3Bb24MM8DggDMOr+fjwSM4\n95x73/dypPuec869RNUKCwtDWFiYpsOoM7p2PkREuoqJChERVSs+Ph7x8fGaDqPO6Nr5EBHpKiYq\nRERERESkdZioEBGRVtu/fz+sra01HQYREb1iTFSIiIiIiEjrMFEhIiIiIiKtw0SFiIiIiIi0DhMV\nIiIiIiLSOkxUiIiIiIhI6zBRISIirTZq1CikpaVpOgwiInrFmKgQEREREZHWaazpAIiISLvl5+ej\nsLAQU6ZMqVW77Oxs6Kl57CIAz7Oza33s6mRnZ6NJkyZ1tj8iIqofHFEhIiIiIiKtwxEVIiKqlqmp\nKUxNTREWFlardlOmTMGzBw/UOrYBAJOWLWt97OrU5egMERHVH46oEBERERGR1mGiQkREWm3//v2w\ntrbWdBhERPSKMVEhIiIiIiKtw0SFiIiIiIi0DhMVIiIiIiLSOkxUiIiIiIhI6zBRISIiIiIircNE\nhYiItNqoUaOQlpam6TCIiOgVY6JCRERERERah2+mJyKianl4eGg6hDqla+dDRKSrmKgQEVG1pkyZ\noukQ6pSunQ8Rka7i1C8iIiIiItI6TFSIiIiIiEjrMFF5hTZt2gSpVFrvx8nKyoKzszNu3rxZZZ3/\n+7//w6JFiwAAjx49glQqRWpqao37vnfvHqRSqdIvW1tbpKSk1Nl5vIzFixdjwIABdbKv5ORkzJo1\nC/3794e9vT08PDwwYcIE7N69W66e7PwPHTqksI958+bB09MTAJCSkgKpVIrQ0NAqjxkUFARbW1vc\nu3dP7fivXbsGqVSKrKysKuvIYt+wYYPS7QMGDIBUKsWCBQvEMmV9oHfv3hg/fjzi4+Pl2icnJ8PJ\nyQn3799X+3yo4dq/fz+sra01HQYREb1iTFReIYlEAolEUu/HCQoKQq9evdC9e/cq69y8eVPcfuPG\nDRgbG6NTp04qH2P69OnYt2+f3FdERAQ6dOigdvzq+PTTT7F582a195OYmIjRo0cjNzcX8+fPR1hY\nGBYuXIiOHTsiNjZWob5EIkFISAhKSkoUymW/c6lUiq5duyIqKqrK48bExMDV1RXt2rVT+xzi4uJg\nZ2eH1q1bV1vP1NQUMTExCuWXLl1CZmYmjI2Nlbar2AcCAwOhr6+P6dOnIzExUaxja2uLPn36YOPG\njeqdDBERETU4XEyvYx49eoSYmBhs2bKlyjpFRUW4ffs27OzsAJQnKra2trU6jqWlJRwcHNSKtT5Y\nWVnVyX5++OEHNG3aFOHh4Wjc+J9/JsOGDVNav0+fPjh37hwiIiIwbty4Kvfr5+eHNWvWiKMrFV24\ncAH37t1DQEBAnZxDbGwsfHx8aqw3cOBAREdH4+LFi3B1dRXLIyMj4ebmhoyMDKXtKvcBd3d3uLi4\n4Ndff5Ur9/f3R0BAAObMmYNWrVqpcUZERETUkHBERcPy8/OxYsUK/Otf/4K9vT3effdd7NixQ6He\nzZs38cEHH8DR0RH9+/fH1q1bERwcrHCze/DgQZiamlb7+M2UlBQIgiC2vXHjRrWjLy9j48aNsLOz\nw40bN8Sy58+fY/DgwRgzZgzKysrE8qioKPj4+MDBwQG9e/fGggUL8PDhQ7n9eXp6Yv78+Thy5AiG\nDh0KZ2dnjBgxApcvX5art2jRInGqVcXjrlu3Dl5eXuIUrlmzZiEnJ6fK+J88eQIzMzO5JKU69vb2\nGDhwIL799lu8ePGiynrDhg2Dnp6e0lGVyMhIGBkZYfDgwWLZzp07MXToUDg6OsLNzQ0jRoxQOqJT\nWWpqKu7evYuBAwfWWLdNmzZwc3OTi6moqAjHjx+Hr68vBEGocR8AYGhoCH19fYVRJQ8PD5iYmODw\n4cMq7YeIiIgIYKKiUYIg4JNPPsHhw4cxdepUbN26Fe+88w5Wr14tt2YgNzcXkyZNwtOnT7F27Vos\nWbIE8fHxiIyMVJhKFh8fDycnJzRqJP+rrbiuwN/fH2VlZXB2doZUKsXJkyexe/duSKVSldd3CIKA\n0tJSua+KycfMmTNhZ2eHuXPn4vnz5wCA5cuXIycnB998840YX0REBBYuXAgbGxuEhIRg3rx5iI+P\nx/jx48V2MpcvX0Z4eDhmz56NoKAglJWVYcaMGcjPzxfrVJ5eV1xcjMmTJ+PHH3/EiBEjsG3bNixd\nuhTm5ubIy8ur8vzs7e2RmpqKZcuWITExEaWlpTVek88//xw5OTnYtWtXlXVatGgBDw8P/Pzzz3IJ\nQFFREU6cOAEvLy+YmJgAAKKjo7F27VoMGzYM33//PdatW4d333232rhl4uLi0L59e9jY2NRYFwB8\nfHxw7NgxFBUVAQB+/fVXlJSUyCVNlVXsAw8ePMCqVatQXFys0EZPTw/Ozs44e/asSrEQERERAZz6\npVGnTp3ClStXsHr1avj6+gIonz5TUFCA8PBwTJ48Gebm5ggPD8eLFy8QGhoKCwsLAOWfUlceOQDK\n11ZMmjRJodzCwgKRkZEAgJUrV8LKygoTJ07EnTt3MHfuXPz4448wNjaGvr6+SrEvXboUX331lVyZ\nsbExrly5AqD85nT9+vXw9fXFihUr4OHhgcjISHzzzTdo27YtAKCsrAzBwcHo3bs31q9fL+6nQ4cO\nGDduHA4ePIgPP/xQLH/27Bmio6NhamoKoPymf+TIkTh9+jTee+89pXFGRUUhISEB3377Lfr16yeW\nDxo0qNrz++ijj5CSkiKuvWnSpAl69uyJd999FyNHjlS61sjGxgbvv/8+tm/fjrFjx4pxVubr64vT\np08jPj4e//rXvwCUJwbPnj0T+wEAJCQkoGvXrpgxY4ZY9s4771Qbt0xcXFytHiowePBgrFixArGx\nsRg6dCiioqLg5eVV5foUQLEPGBoaYsWKFXByclKoa2tri7CwMJXjISIiImKiokGXLl2Cnp4e3n//\nfblyb29vHDhwANeuXUO/fv2QkJAAR0dHMUkBym8K+/btKzed5smTJygsLETz5s0VjqWvry9O9bpz\n5w4mTJgAqVSK69ev46233kKPHj1qFfunn36qkCjp6enJ/dyuXTssX74cc+bMwS+//AI/Pz8MHTpU\n3H737l08evQIs2fPlmvXs2dPtG3bFhcuXJBLVJycnORu/rt06QIAyMzMrDLO8+fPo2XLlnJJiioM\nDQ2xadMmpKam4syZM7h69Sp+//13nDt3DsePH8f27duVtvvss89w5MgRhIaG4vPPP1dax9PTE02b\nNkVUVJSYqERGRsLCwgJvv/22WM/e3h579+7FypUrMWDAADg7O6NJkyY1xv7w4UMkJiaKT3VThbGx\nsbhWxc3NDefOncP3339fbZuKfSAvLw+//fYbvvrqKzRp0kTu9wwAzZs3R1FRER4/fgxzc3OV46LX\nx4IFC5CdnS1Xlp2djTIAO0tKIBs/rJziy8p3/v8pgxIARhW2FwAYNWoUE10iogaIiYoG5eXlKV0H\n0bJlSwDA48ePAZTfeMpuypXVk5GtjTAwMFCoK5u6lJaWhpycHDg6OqKkpASXL1+Gg4ODuL1yslGV\nNm3aqLSupW/fvuI0q4kTJ8ptk52fsgXWrVq1UpjiZGZmJvez7DyrWxPy+PHjGp96VZ1OnTqhU6dO\nmDx5MoqKirBkyRLExMTg9OnT6Nu3r0J9KysrjBw5Ert27cL48eOV7tPAwABDhgxBVFQUCgoK8Pz5\nc5w/fx5Tp06VG6nx9fVFUVERDhw4gL1790JPTw99+/bFokWLqn0qWGxsLFq0aAFnZ+danauvry+m\nTZuGHTt2oEWLFnJJkzKV+4C7uzvS09MRGBiokKjIEqzqflf0esvOzsaDBw+hZ/hPmiHRbwLZX5TS\nF+VTORsZGsm1Kyspn27YqLEBSl88h6SRBCYV/raZQPFvHRERNQxMVDTIzMwMeXl5KCkpkUtWZJ9K\nNmvWDED5TfujR48U2ldecC77pPrJkycKdSveUEokErmbbIlEgpiYGEgkEiQnJ6txRoqWL18OQRDw\n1ltvYenSpfjpp5/E9SmyeCt/CguUn5vsqWTqaNasGf744w+19wOUJxhTp05FdHQ0bt++rTRRAcpH\nGiIjI7F169Yq9+Xn54eIiAgcP34ceXl5KCsrU/qELn9/f/j7++Pp06eIj4/H6tWrMWfOHERERFS5\n77i4OPTv37/W5+fu7o7mzZuL0w5f5lHaNjY2OHPmDHJycuRG9mRJqaxPk27SMzSCRU9vpdseXI4G\ngCq3y+q0MDPl6AkREQHgYnqNcnNzQ2lpKY4dOyZXHh0dDQMDAzg6OgIon/J07do1uRf3FRYW4syZ\nM3Lt9PX1YWlpifT0dIVjHTx4EAcPHkSvXr0wdOhQHDp0CCEhIZBIJNi+fTsOHjyIAwcO1On5xcTE\nICYmBv/+97/xzTffICkpSe59Gh06dEDLli1x5MgRuXZXrlxBZmYmevXqpXYMffr0QXZ2Nk6dOlWr\ndpWTQBnZSzGre8yuhYUFPvjgA+zdu7fKFx06Ojqiffv2iIyMRFRUFLp3717te2zeeOMNDBkyBEOG\nDMGtW7eqrJefn4/ff/9dpad9VSaRSBAQEID+/ftj+PDhtW4PlD9RTl9fX2F9TkZGBtq0aaN0tI+I\niIhIGY6oaNA777yDnj17YtmyZXj06BE6d+6MU6dO4eDBg5g2bZo44jBp0iTs3bsXU6dORUBAAPT1\n9bFz504YGhoqfOrt6uoq98I9me7du6OsrAwpKSlYtWoVunXrhsTERHTs2BF9+vSpdezp6elISEhQ\nKLe2toaZmRnS09OxfPlyjBo1Sly4Pnv2bKxfvx7u7u7o1asXGjVqhFmzZmHZsmWYP38+vL29cf/+\nfWzcuBEdOnTAiBEjah1XZd7e3ti3bx/mzJmDTz75BI6OjsjPz0d8fDwmTZpU5Qsqv/rqKzx79gyD\nBg1C586dUVZWhsTERISGhqJ9+/bw8vKq9riffPIJ9u3bh4sXL1Y5TcvPzw9BQUEAgCVLlihsX7p0\nKUxMTODk5IQWLVrg7t27cutalDl9+jQMDAxqnLZVldGjR2P06NEq1a3YB/Ly8hAXF4fz589j3Lhx\nCglJYmIiXFxcXiomIiIiapiYqLxiFRMLiUSCbdu2YcOGDdi+fTseP36Mdu3aYfHixZgwYYJYr1mz\nZti5cydWrlyJRYsWwdzcHGPGjEFOTg6io6Pl9i9b+5CZmSk+XUvm2rVrKCgoQO/evQEAZ8+erXL6\nUk3nsG3bNmzbtk1h28aNGzFgwADMnz8fFhYW+PLLL8VtU6dOxfnz57Fw4UJERUXBzMwM/v7+MDIy\nQmhoKAICAmBsbIx+/fph3rx5cgvHKz92uLryij83btwY4eHhCAkJwb59+7B582aYm5ujR48eCmte\nKho/fjx+/vln7NmzBw8ePEBxcTHefPNN+Pj44NNPP4WRUYV5+EpiMDc3x6RJk7B58+Yqp1D5+Pgg\nODgYenp6Sp9a1qNHDxw6dAjR0dF4+vQpLCws4Ovri5kzZ1YZd2xsLDw8PFQeuajqutZUp3IfMDU1\nhZWVFZYtWwZ/f3+5uvfv30dKSgq++OILlWIiIiIiAgCJoOrb3EirlJWVwc/PT1xTICMIAgYPHozh\nw4dj+vTpGoyQXrXi4mK4u7tj2bJlCk+S06Rt27YhIiICsbGxNSZFGRkZGDBgAOLi4mBpafmKIqS6\nMGXKFDzKy6+XNSrW1tYAyh8GQkSK+LeTdBVHVF4TGzduRPv27dG2bVvk5uZi//79uHXrlsIjZCUS\nCT777DOsWbMGkydPhqGhoYYipldNX18fFy9e1HQYcoqKirB7927Mnz//pRbnExERUcPFROU1IZFI\nsGXLFjx48AASiQRdu3bFli1b4OHhoVB32LBhePDgATIyMqpdoE1U3zIyMjBx4kR4e1f9KToRERGR\nMkxUXhOzZs3CrFmzVK4/derUeoyGSDUdO3ZEx44dNR0GERERvYb4eGIiIiIiItI6TFSIiEgUFham\nEy9c1JXzICJqyJioEBGRKD4+HvHx8ZoOQ05aWlqtn/iljedBRES1w0SFiIiIiIi0DhMVIiIiIiLS\nOkxUiIiIiIhI6zBRISIiIiIircNEhYiIiIiItA4TFSIi0mrW1tawtrbWdBhERPSKMVEhIiIiIiKt\nw0SFiIiIiIi0DhMVIiIiIiLSOo01HQAREWmP/Px8FBYWYsqUKbVql52dDUGi3mdfZSVFyM7OVjh2\nly5dAKBWMWVnZ6NJkyZqxUNERJrFERUiIiIiItI6HFEhIiKRqakpTE1NERYWVqt2U6ZMwaO8fLWO\n3aixAVqY1f7YVcVDRESvN46oEBERERGR1mGiQkREREREWoeJChERERERaR0mKkREREREpHWYqBAR\nERERkdZhokJERFrN2toa1tbWmg6DiIheMSYqRERERESkdZioEBERERGR1mGiQkREREREWodvpici\nIpGHh4emQ6gTunIeREQNGRMVIiISTZkyRdMh1AldOQ8iooaMiQoREWm1tLQ0TYdAREQawDUqRERE\nRESkdZioEBERERGR1mGiQkREREREWoeJChERERERaR0mKkREREREpHWYqBARkVaztraGtbW1psMg\nIqJXjIkKERERERFpHb5HhYiI6kTpi+d4cDm6ym0Aqtz+Tx3T+giNiIheQ0xUiIhIbS1btqx2e35+\n+X9NTatLRExr3A8RETUcTFSIiEhta9eu1XQIRESkY7hGhYiIiIiItA5HVIiISKulpaVpOgQiItIA\njqgQEREREZHWYaJCRERERERah4kKERERERFpHSYqRERERESkdZioEBERERGR1mGiQkREWs3a2hrW\n1taaDoOIiF4xJipERERERKR1+B4VItIapaWlAID79+9rOBLSRhkZGZoOgUgryf5myv6GEukKJipE\npDUePnwIABg3bpyGIyFtYmhoCAAYMGCAhiMh0m4PHz5E+/btNR0GUZ2RCIIgaDoIIiIAKCwsxI0b\nN9CqVSvo6elpOhwiotdCaWkpHj58CDs7OzRp0kTT4RDVGSYqRERERESkdbiYnoiIiIiItA4TFSIi\nIiIi0jpMVIiIiIiISOswUSEiIiIiIq3DxxMTUb27f/8+AgMDcf78eQiCAHd3d3z55Zdo06ZNjW2L\nioqwYcMGxMTE4OnTp7C1tcW8efPg4uLyCiKve+pcC6lUqlAmkUhw+PBhpdu0XVZWFrZt24abN28i\nJSUFhYWF+O2339C2bdsa2+pav1DnWuhSvzh27BhiYmJw8+ZN5Obmok2bNhg0aBCmTZsGExOTatvq\nWp9Q51roUp+gho1P/SKielVYWAhvb28YGhpi9uzZAIANGzbgxYsXiI6OrvFRmnPnzsXZs2exYMEC\nWFpa4scff8SZM2cQERHx2v0PV91rIZVKMWLECIwePVquvGvXruK7Rl4nFy5cwJw5c9C9e3eUlpbi\n3LlziIuLU+nmXJf6BaDetdClfjF69Gi0bt0aXl5eePPNN5GcnIxNmzahU6dO+Omnn6ptq2t9Qp1r\noUt9gho4gYioHu3YsUPo1q2b8Ndff4ll6enpQrdu3YTw8PBq2yYnJwtdu3YVDh8+LJaVlJQIgwcP\nFmbMmFFfIdcbda6FIAhC165dhaCgoHqMUHP27dsnSKVS4d69ezXW1bV+UVltroUg6Fa/yMnJUSg7\nfPiwIJVKhf/+979VttPFPvGy10IQdKtPUMPGNSpEVK9OnjwJR0dHWFlZiWWWlpbo0aMH4uLiqm0b\nFxcHfX19DBkyRCzT09PDe++9h/j4eBQXF9db3PVBnWtB/9C1fkH/aNasmUKZvb09BEFAVlZWle10\nsU+87LUg0iVMVIioXt2+fRudO3dWKLexsUFqamq1bVNTU2FpaakwVcHGxgbFxcX466+/6jTW+qbO\ntZDZu3cv7O3t4eTkhIkTJ+LSpUt1HabW07V+URd0uV9cuHABEokEnTp1qrJOQ+kTqlwLGV3uE9Rw\ncDE9EdWrx48fw8zMTKHczMwMT548qbZtXl6e0rbm5ubivl8n6lwLAPDx8UG/fv1gYWGBzMxMhIaG\nYtKkSQgPD4erq2t9hKyVdK1fqEuX+0VWVhY2bdoEd3d3dO/evcp6DaFPqHotAN3uE9SwMFEhInpN\nrFmzRvy+Z8+e8PT0xLBhw7Bx40b88MMPGoyMNElX+0VBQQFmzJgBfX19BAYGajocjarttdDVPkEN\nD6d+EVG9MjMzQ15enkJ5Xl4emjZtWm3bpk2bKm0r+3RU9mnp60Kda6GMiYkJ+vbti+vXr9dFeK8N\nXesXdU0X+sWLFy8wbdo03Lt3D6GhoWjdunW19XW5T9T2WiijC32CGiYmKkRUr2xsbHD79m2F8tu3\nb9c4z9rGxgYZGRl48eKFQlt9fX289dZbdRprfVPnWtA/dK1fkLySkhJ89tlnSEpKwvfffw8bG5sa\n2+hqn3iZa0GkS5ioEFG98vT0REJCAjIyMsSyjIwMXL16FQMGDKixbXFxMY4ePSqWlZaW4ujRo/Dw\n8IC+vn69xV0f1LkWyuTn5+PUqVNwcHCoyzC1nq71i7r2OvcLQRAwd+5cXLhwAVu2bFH5HHSxT7zs\ntVDmde4T1LDpff31119rOggi0l1du3bFkSNHcPz4cVhYWODu3btYtmwZjIyMsHLlSvEGIjMzE716\n9YJEIhEXe7Zq1Qp37tzBnj17YG5ujidPnmDdunW4fv061q1bh5YtW2ry1GpNnWsRFhaGyMhIPH/+\nHLm5ubhw4QKWLFmCzMxMrFmzRqU322uj48ePIzU1FZcvX8aNGzdgbW2NzMxM5Obmol27dg2iX8i8\nzLXQtX7x9ddfIyoqCh9//DFsbGyQlZUlfkkkEpiamjaYPvGy10LX+gQ1bFxMT0T1ysjICDt37kRg\nYCAWLlwIQRDg7u6OxYsXw8jISKwnCIL4VdHq1auxYcMGbNy4EU+fPoVUKkVoaOhr+aZpda5Fhw4d\nEBsbixMnTuDp06cwNTVFz549sWrVKtjZ2WnidOrE559/DolEAgCQSCRYsWIFAMDV1RW7du1qEP1C\n5mWuha71i7Nnz0IikeC7777Dd999J7ctICAAM2fObDB94mWvha71CWrYJELlf+lEREREREQaxjUq\nRERERESkdZioEBERERGR1mGiQkREREREWoeJChERERERaR0mKkREREREpHWYqBARERERkdZhokJE\nRERERFqHiQoREQEAQkJCIJVKIZVKERISorBdts3W1lZpecXtLi4uGDt2LA4fPqzy8dPS0jB9+nS4\nu7vD1tYWUqkUu3btUvu8VFHx3KVSKVauXKlQZ8WKFXJ1lF2jupCSkoKQkBCEhIQgJSXlpfdz7949\nMdbFixeL5YsWLRLLMzMzxfKdO3ciJCQEO3fuVCt+IqK6wjfTExGRHNnb0WuzrXL5s2fPcPXqVVy9\nehXPnj3Dhx9+WONxFyxYgMTERHFfjRq9+s/SJBIJBEFAVFQU5s2bhyZNmgAAnj9/jujo6GqvTV1J\nTk5GSEgIJBIJLC0t1X6zeuWYJRKJ+FXRzp07kZmZiXbt2mHixIlqHZOIqC5wRIWIiFQmCEK15cnJ\nybh27RoCAgIAlN8U7969W6V9JyUlQSKRoGPHjkhISEBSUhImTJhQN4EDKC4uVrlufn4+YmJixJ+j\no6ORn58PoOproK6ioqJ62W9lq1atQnJyMpKSktC2bVu5ba8iESMiUhUTFSIiqlOGhoaYNGkSgPKb\n+r///rva+ocPH4ZUKkVpaSkEQUBqaiocHBwglUpx8eJFAEBubi4CAwMxaNAg2Nvbo0ePHhgzZgwO\nHTokt68LFy6I05qCg4Px3XffwdPTE926dcO1a9dUir9du3YQBAE//fSTWBYREQGJRIJ27dpV2e7S\npUuYMWMG3n77bdjZ2cHDwwNz5szB//73P7l6ixcvFmO8dOkSZs2aBRcXFwwZMgTjx4/H4sWLxZGd\nitO0IiMjAQDBwcEYM2YM+vTpAzs7Ozg7O8Pb2xtbt25VKRmrPPVLds0yMzMhCILclDFPT0/ExsaK\nP2/fvl1uX2vWrBG3JSQkqHR9iYhUxalfRERUr1q0aFFjnYqf5Mu+l/03Ozsb/v7+yMzMFMtKSkpw\n7do18WvFihUK+9uzZw/y8vIU9l8TPz8/bN26FUlJSbh+/ToEQUBSUhIMDAwwfPhwBAcHK7SJiorC\n4sWLUVZWJh7r0aNHOHLkCGJjYxEaGgpXV1eFGGfOnCnGaGZmpjAlq/K1AICjR48iLS1N/Lm0tBR/\n/PEHNmzYgD///BOBgYHVnp+yfcq+FwRBrrxRo0YYOHAgrKyskJGRgX379uGjjz6Si0UikaBz585w\ndHSs9rhERLXFERUiIlJQeXG5VCpV+Wa/sLAQ4eHhAMpvgN9///1q6/v5+SE5OVm8SXZ1dRWnJrm6\nuiIoKEhMUoYPH47ff/8dUVFR4rSl/fv3Kx0tycvLw5IlS3Dp0iWcPHkSXbp0USn+Zs2aYdCgQQCA\nPXv2YO/evQAALy8vNG/eXKH+8+fP8Z///AeCIKBx48bYvHkzLl++jOXLlwMon3K2dOlSpcd64403\nEBERgYSEBGzbtg27du1CYGCgeC0qTtPy9fUFAMydOxe//PILLl26hBs3buDEiRPiOpaoqCg8efJE\npfOUcXNzQ3JyMtq0aQMAaNu2LZKTk5GcnIzY2FgAwIcffghBEJCeno5z584BKB9Bun//PgBg1KhR\ntTomEZEqmKgQEZGCiguulS28VlZfEARIpVI4OTlh8+bNaNy4Mfz9/fH555+rFcvp06fF7xcuyVb3\nHQAABJVJREFUXIimTZuiS5cu4vSyynVk3N3dMW7cOJiYmKB169YwMzNT+Zhjx46FIAg4evSoOGow\nduxYpXWvXLkiJgd9+/aFp6cnjI2N4e/vD1tbWwiCgLS0NKSnpyu0nT17NhwcHGBgYIBOnTqpFJuJ\niQkCAwPh5eUFBwcHeHl5ITk5GQBQVlYmN9pSV0aOHAlTU1MAEKfE/fLLLwAAAwMDeHt71/kxiYiY\nqBARkYKAgADxU3XZlyqLyCsmNoIgoKCgQO1YcnNzAQDGxsZo2rSpWF5xIfijR48U2nXr1u2lj+ni\n4oLOnTujsLAQhYWFsLGxgYuLi9K6OTk54veyUQlVY6ztE72uXLmCqVOnIj4+Hrm5ueJUs4qJ5IsX\nL2q1T1WYmJhgxIgREAQBJ0+exN9//40TJ05AIpFg4MCBtUoCiYhUxUSFiIjUJpuqlJycjFOnTsHN\nzQ2lpaX4+eefsXbtWrX2LZtuVVBQgKdPn4rlFRfpK1sHY2hoqNZxx4wZAwDVjqZUPnblBwfUFKPs\n8ccVVTd6dezYMTE5+fjjj3H16lUkJyfDy8ur6hNRUU2jZuPHj0ejRo1QWlqKBQsWiIkXp30RUX1h\nokJERHWqdevWWLt2LYyMjACUr/O4c+fOS++vX79+4vdr1qzBkydPcOvWLezYsUNpnbri4+ODQYMG\nwcvLCz4+PlXWc3Z2hpmZGQRBwJkzZ3Dy5EkUFBRg3759SEpKAgB07NgRVlZWKh3X3Nxc/P7WrVso\nLS0Vf9bT0xO/NzY2RqNGjXDq1CmlU99qS3bc3NxcZGVlKWy3tLSEp6cnBEEQn8ZmZWWF3r17q31s\nIiJlmKgQEVGda926NSZMmABBEFBaWooNGzao1E7Z9LJZs2aJjwU+cOAA3Nzc4O3tjXv37kEikWDM\nmDH18sQpU1NTBAcHIzg4GCYmJlXWMzIywpIlS6Cnp4eSkhLMmDEDPXr0wNKlSyGRSGBoaCgurFeF\nra0t9PX1AQBhYWHo3r27+PjggQMHiiMfQUFBcHBwQEBAAN588031ThaAk5MTgPKRq759+yq80R6A\nuC5INt1s5MiRah+XiKgqTFSIiEhU01vplS2sr6r8448/Fj+lj42NRWJiYo3HVrafli1b4uDBg5g4\ncSLat28PAwMDmJiYwMnJCatWrcKyZctUPoeajv+y9YYNG4Zdu3ahX79+aNasGRo3boyWLVti6NCh\n2L9/v8L6luoeUCAbkbKxsYGhoSEkEgkaNSr/33XPnj2xfv16dOzYEYaGhujcuTOCgoLQo0ePKn83\nymJWVnfmzJl477330KJFiyp/Fy4uLujWrRsEQYCenh78/PxUuGJERC9HItTXK3aJiIhIpxQUFMDX\n1xfp6enw8vJS+k4ZIqK6whc+EhERUbWysrIwceJEZGdnIz8/H40bN8aMGTM0HRYR6TgmKkRERFSt\nkpIS/Pnnn9DT04ONjQ2++OIL2NraajosItJxnPpFRERERERah4vpiYiIiIhI6zBRISIiIiIircNE\nhYiIiIiItA4TFSIiIiIi0jpMVIiIiIiISOswUSEiIiIiIq3z/wCeGLDsej/b3AAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb988a8bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{univariate-metrics-os}}}')\n",
    "coefdata = univariate_coefplot(univariate_os_models,\n",
    "                               metrics=['missense_snv_count','exonic_snv_count','neoantigen_count'])\n",
    "_ = plt.xlim([0, 2.5])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:27:09.499962",
     "start_time": "2016-08-17T08:27:06.511328"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-ratios-os}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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CwMDAt1pPRkYGlEplWZeHpUuXQqFQvLGd+viJiIjQeCw5ORkKhQJZWVllXt+H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\nut805NzjhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb988a9edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look at expression ratios\n",
    "print('{{{univariate-ratios-os}}}')\n",
    "coefdata = univariate_coefplot(univariate_os_models,\n",
    "                               metrics=[x for x in extra_cols if 'expression_ratio' in x], hue=None)\n",
    "_ = plt.xlim([0, 2.5])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['Expression Rate of Missense SNVs',\n",
    "                'Expression Rate of Predicted Neoantigens',\n",
    "                'Expression Rate of Exonic SNVs',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{univariate-ratios-os-original}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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tbmHNmjVx79490TJLS0uNS7c5OTlQqVRaL73KZDI8fPhQqKOwsBA7duzA1q1bYWhoCB8f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb979771250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look at expression ratios, separately\n",
    "## on original scale \n",
    "coefdata = univariate_coefplot(univariate_os_models,\n",
    "                               metrics=[x for x in extra_cols if 'expression_ratio' in x],\n",
    "                               hue=None)\n",
    "plt.close()\n",
    "print('{{{univariate-ratios-os-original}}}')\n",
    "sb.boxplot(data=coefdata,\n",
    "           x='value',\n",
    "           y='metric',\n",
    "           fliersize=0, whis=[2.5, 97.5])\n",
    "_ = plt.vlines(x=0, ymin=-10, ymax=10, linestyles='--')\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['Expression Rate of Missense SNVs',\n",
    "                'Expression Rate of Predicted Neoantigens',\n",
    "                'Expression Rate of Exonic SNVs',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('log(HR) for {}'.format(cohort.hazard_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{compare_exonic_snv_count_os:median HR of 0.89 vs. 0.93 for expressed versus total}}}\n",
      "{{{compare_missense_snv_count_os:median HR of 0.88 vs. 0.92 for expressed versus total}}}\n",
      "{{{compare_neoantigen_count_os:median HR of 0.91 vs. 0.97 for expressed versus total}}}\n"
     ]
    }
   ],
   "source": [
    "univariate_os_coefdata = extract_univariate_coefdata(univariate_os_models,\n",
    "                                                 metrics=['missense_snv_count','exonic_snv_count','neoantigen_count']\n",
    "                                                 )\n",
    "\n",
    "\n",
    "coefdata_os_grouped = univariate_os_coefdata.groupby('metric')\n",
    "for name, group in coefdata_os_grouped:\n",
    "    hyper_label='compare_%s_os' % name\n",
    "    paper.hyper_label_printer(label=hyper_label, formatter=paper.compare_posterior_dist, df=group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# univariate logistic analysis of revised metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:27:09.524216",
     "start_time": "2016-08-17T08:27:09.501719"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../utils/stan/logistic_model.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_expressed_missense_and_neoant_mutations.stan\n",
      "../utils/stan/logistic_model_by_group.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef_hsprior.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_alt.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs3.stan\n",
      "../utils/stan/pem_survival_model_randomwalk.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_rate_only.stan\n",
      "../utils/stan/pem_survival_model_gamma.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_missense_and_neoant_rates.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs4.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline.stan\n",
      "../utils/stan/pem_survival_model_randomwalk2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline2.stan\n"
     ]
    }
   ],
   "source": [
    "models = survivalstan.utils.read_files('../utils/stan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:27:09.546805",
     "start_time": "2016-08-17T08:27:09.526255"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "logit_pfs = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df,\n",
    "    model_code = models['logistic_model.stan'],\n",
    "    time_col = 'pfs', # not used, included for consistency\n",
    "    event_col = 'is_benefit',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    seed = seed,\n",
    "    )\n",
    "logit_os = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df,\n",
    "    model_code = models['logistic_model.stan'],\n",
    "    time_col = 'os', # not used, included for consistency\n",
    "    event_col = 'is_benefit_os',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    seed = seed,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:27:09.566300",
     "start_time": "2016-08-17T08:27:09.548766"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#test_model = logit_pfs(formula = '~ missense_snv_count', model_cohort = 'test model')\n",
    "#survivalstan.utils.plot_coefs([test_model])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analysis for DCB using PFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:28:53.326443",
     "start_time": "2016-08-17T08:27:09.567917"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 48.878 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.889 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.630 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.907 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.946 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.947 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.865 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.896 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.065 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.975 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.673 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.740 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.930 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.845 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.130 sec.\n"
     ]
    }
   ],
   "source": [
    "univariate_logit_models = list()\n",
    "for var in vars_centered:\n",
    "    univariate_logit_models.append(logit_pfs(formula = var, model_cohort = 'univariate logit model for PFS-benefit'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:28:54.189346",
     "start_time": "2016-08-17T08:28:53.328352"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#survivalstan.utils.plot_coefs(univariate_logit_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{coefplot-dcb-pfs2}}}\n"
     ]
    },
    {
     "data": {
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s7AwXFxcMGDAA+/fvF9anpKQgNDQU3bp1g6OjIwICArB06VKlctPT0zFr1ix4enrC3t4e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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb948bf4b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{coefplot-dcb-pfs2}}}')\n",
    "coefdata = univariate_coefplot(univariate_logit_models,\n",
    "                               metrics=['missense_snv_count','exonic_snv_count',\n",
    "                                        'neoantigen_count','peripheral_clonality_a',\n",
    "                                        'tcell_fraction',\n",
    "                                       ])\n",
    "_ = plt.yticks([0, 1, 2, 3, 4],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "                'log(Baseline Peripheral TCR Clonality)',\n",
    "                'log(TIL Fraction)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('Odds Ratio for {}'.format(cohort.benefit_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T15:15:01.855804",
     "start_time": "2016-08-17T15:14:59.760792"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{coefplot-dcb-pfs}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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2z+NlL0+Wl0gk8PDwUHodFRUFiUSi1MOgirKb0SNHjmDZsmU1qtuvXz/069cP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb948b125d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{coefplot-dcb-pfs}}}')\n",
    "coefdata = univariate_coefplot(univariate_logit_models,\n",
    "                               metrics=['missense_snv_count','exonic_snv_count',\n",
    "                                        'neoantigen_count'])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('Odds Ratio for {}'.format(cohort.benefit_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{compare_exonic_snv_count_logit_dcb:median OR of 1.35 vs. 1.32 for expressed versus total}}}\n",
      "{{{compare_missense_snv_count_logit_dcb:median OR of 1.38 vs. 1.34 for expressed versus total}}}\n",
      "{{{compare_neoantigen_count_logit_dcb:median OR of 1.26 vs. 1.20 for expressed versus total}}}\n"
     ]
    }
   ],
   "source": [
    "univariate_logit_dcb_coefdata = extract_univariate_coefdata(univariate_logit_models,\n",
    "                                                 metrics=['missense_snv_count','exonic_snv_count','neoantigen_count']\n",
    "                                                 )\n",
    "\n",
    "\n",
    "coefdata_logit_dcb_grouped = univariate_logit_dcb_coefdata.groupby('metric')\n",
    "for name, group in coefdata_logit_dcb_grouped:\n",
    "    hyper_label='compare_%s_logit_dcb' % name\n",
    "    paper.hyper_label_printer(label=hyper_label, formatter=paper.compare_posterior_dist, df=group, stat='OR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T15:15:26.393862",
     "start_time": "2016-08-17T15:15:25.953583"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{coefplot-ratios-dcb-pfs}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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ioneLI7taaNSokVYXqPXq1Qtbt26FhYWFaKSsLE1zC2vXro07d+6Ilpmbm6udus3Ly4NS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\n3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb98821ee90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look at expression ratios, separately\n",
    "print('{{{coefplot-ratios-dcb-pfs}}}')\n",
    "\n",
    "coefdata = univariate_coefplot(univariate_logit_models,\n",
    "                               metrics=[x for x in extra_cols if 'expression_ratio' in x],\n",
    "                               hue=None)\n",
    "_ = plt.xlim([0, 5])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['Expression Rate of Missense SNVs',\n",
    "                'Expression Rate of Predicted Neoantigens',\n",
    "                'Expression Rate of Exonic SNVs',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('OR for {}'.format(cohort.benefit_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{summary_exonic_expression_ratio_logit_dcb:OR=1.64, 95% CI (0.17, 6.77)}}}\n",
      "{{{summary_missense_expression_ratio_logit_dcb:OR=1.96, 95% CI (0.22, 7.76)}}}\n",
      "{{{summary_neoantigen_expression_ratio_logit_dcb:OR=2.34, 95% CI (0.25, 9.34)}}}\n"
     ]
    }
   ],
   "source": [
    "univariate_logit_dcb_coefdata2 = extract_univariate_coefdata(univariate_logit_models,\n",
    "                                                 metrics=[x for x in extra_cols if 'expression_ratio' in x]\n",
    "                                                 )\n",
    "\n",
    "\n",
    "coefdata_logit_dcb_grouped2 = univariate_logit_dcb_coefdata2.groupby('metric')\n",
    "for name, group in coefdata_logit_dcb_grouped2:\n",
    "    hyper_label='summary_%s_logit_dcb' % name\n",
    "    paper.hyper_label_printer(label=hyper_label, formatter=paper.hr_posterior_formatter, series=group['exp(beta)'], stat='OR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analysis for DCB using OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:29:47.699556",
     "start_time": "2016-08-17T08:28:56.548455"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.117 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.045 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.926 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.125 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.806 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.741 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.765 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.880 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 2.927 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.065 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.142 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.217 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.196 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.153 sec.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 3.046 sec.\n"
     ]
    }
   ],
   "source": [
    "univariate_logit_models_os = list()\n",
    "for var in vars_centered:\n",
    "    univariate_logit_models_os.append(logit_os(formula = var, model_cohort = 'univariate logit model for OS-benefit'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{coefplot-dcb-os2}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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DIUOGIDExUamMYqjiH3/8gcmTJ8PBwQG+vr7YvHmzUrnCwkKsWLECPj4+sLGxgbu7O0JD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\nky4DRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb939cf9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{coefplot-dcb-os2}}}')\n",
    "coefdata = univariate_coefplot(univariate_logit_models_os,\n",
    "                               metrics=['missense_snv_count',\n",
    "                                        'exonic_snv_count',\n",
    "                                        'neoantigen_count',\n",
    "                                        'peripheral_clonality_a',\n",
    "                                        'tcell_fraction',\n",
    "                                       ])\n",
    "_ = plt.yticks([0, 1, 2, 3, 4],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "                'log(Baseline Peripheral TCR Clonality)',\n",
    "                'log(TIL Fraction)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('OR for {}'.format(cohort.benefit_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T15:14:35.320940",
     "start_time": "2016-08-17T15:14:28.029745"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{coefplot-dcb-os}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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dd99h2bJltarbt29f9O3bFwUFBbh69So+//xzTJ06FT/88AMAQCaTYcuWLSgv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\nVSNHWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb92896e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{coefplot-dcb-os}}}')\n",
    "coefdata = univariate_coefplot(univariate_logit_models_os,\n",
    "                               metrics=['missense_snv_count','exonic_snv_count','neoantigen_count'])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['log(# Missense SNVs / MB)',\n",
    "                'log(# Predicted Neoantigens / MB)',\n",
    "                'log(# Exonic SNVs / MB)',\n",
    "               ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('OR for {}'.format(cohort.benefit_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{coefplot-ratios-dcb-os}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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3IJfLsWvXLmHZtGnT4Ofn91brycjIgEKhqOjysHz5csjl8nLbqY6fqVOnqj2W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\ncOEC9PT0VGG3rmMIgoAtW7Y0OPA25Vp5enri559/1lgDAJw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb928883990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## look at expression ratios, separately\n",
    "print('{{{coefplot-ratios-dcb-os}}}')\n",
    "\n",
    "coefdata = univariate_coefplot(univariate_logit_models_os,\n",
    "                               metrics=[x for x in extra_cols if 'expression_ratio' in x],\n",
    "                               hue=None)\n",
    "_ = plt.xlim([0, 5])\n",
    "_ = plt.yticks([0, 1, 2],\n",
    "               ['Expression Rate of Missense SNVs',\n",
    "                'Expression Rate of Predicted Neoantigens',\n",
    "                'Expression Rate of Exonic SNVs',\n",
    "                 ])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.xlabel('OR for {}'.format(cohort.benefit_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{compare_exonic_snv_count_logit_os:median OR of 1.25 vs. 1.17 for expressed versus total}}}\n",
      "{{{compare_missense_snv_count_logit_os:median OR of 1.25 vs. 1.20 for expressed versus total}}}\n",
      "{{{compare_neoantigen_count_logit_os:median OR of 1.17 vs. 1.08 for expressed versus total}}}\n"
     ]
    }
   ],
   "source": [
    "univariate_logit_os_coefdata = extract_univariate_coefdata(univariate_logit_models_os,\n",
    "                                                 metrics=['missense_snv_count','exonic_snv_count','neoantigen_count']\n",
    "                                                 )\n",
    "\n",
    "\n",
    "coefdata_logit_os_grouped = univariate_logit_os_coefdata.groupby('metric')\n",
    "for name, group in coefdata_logit_os_grouped:\n",
    "    hyper_label='compare_%s_logit_os' % name\n",
    "    paper.hyper_label_printer(label=hyper_label, formatter=paper.compare_posterior_dist, df=group, stat='OR')"
   ]
  }
 ],
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